STIMULATION-BASED SYSTEMS AND METHODS FOR TREATING BLADDER AND/OR BOWEL DYSFUNCTION
[001] A portion of the population suffers from bladder and/or bowel dysfunction, such as one or both of urinary incontinence (or bladder incontinence) and fecal incontinence (or bowel incontinence). Diet, training, slings, and drug therapies may fail to treat incontinence.
Brief Description of the Drawings
[002] FIG. 1 is a schematic illustration of anatomy of a human pelvic region.
[003] FIG. 2 is a schematic illustration of the pelvic region of FIG. 1 and various nerves.
[004] FIG. 3 is a block diagram of a treatment system in accordance with principles of the present disclosure.
[005] FIGS. 4A and 4B are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[006] FIG. 5 is a graph illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[007] FIG. 6 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[008] FIG. 7 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[009] FIG. 8 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[010] FIG. 9 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[Oil] FIG. 10 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information. [012] FIGS. 11 A and 11 B are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[013] FIG. 12 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[014] FIG. 13 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[015] FIG. 14 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[016] FIG. 15 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[017] FIG. 16 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[018] FIG. 1 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[019] FIG. 18 is a diagram identifying external sources of information useful with some algorithms of the systems and methods of the present disclosure.
[020] FIG. 19 is a diagram illustrating a fingerprint catalog or library in accordance with principles of the present disclosure.
[021] FIG. 20 are graphs illustrating algorithms useful with systems and methods of the present disclosure for applying stimulation based on sensor information.
[022] FIG. 21 is a graph illustrating an example fingerprint useful with systems and methods of the present disclosure.
[023] FIG. 22 is a graph illustrating an example fingerprint useful with systems and methods of the present disclosure.
[024] FIG. 23 is a flow diagram of a method for learning fingerprints in accordance with principles of the present disclosure.
[025] FIG. 24 is a simplified illustration of a treatment system in accordance with principles of the present disclosure as used by a patient. [026] FIG. 25 is a simplified illustration of a treatment system in accordance with principles of the present disclosure as used by a patient.
[027] FIG. 26 is a flow diagram of a method for assessing and/or adjusting triggering or predictive algorithms in accordance with principles of the present disclosure.
[028] FIG. 27 is a simplified illustration of a treatment system in accordance with principles of the present disclosure as used by a patient.
[029] FIG. 28 is a simplified illustration of a treatment system in accordance with principles of the present disclosure as used by a patient.
[030] FIG. 29 is a simplified illustration of a treatment system in accordance with principles of the present disclosure as used by a patient.
[031] FIG. 30 is a graph illustrating a therapeutic or exercise mode of operation useful with some systems and methods of the present disclosure.
Detailed Description
[032] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized, and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense. It is to be understood that features of the various examples described herein may be combined, in part or whole, with each other, unless specifically noted otherwise.
[033] At least some examples of the present disclosure are directed to implantable devices for diagnosis, therapy, and/or other care of medical conditions. At least some examples may comprise implantable devices, methods of implanting devices and/or method of operating an implanted device useful for treating bladder or bowel dysfunctions, including one or both of urinary incontinence and fecal incontinence of a patient, or other pelvic disorders. At least some such examples comprise implanting an electrode to deliver a nerve-stimulation signal to one or more nerves or nerve branches to activate a corresponding external sphincter, such as a branch of the pudendal nerve that activates the external urethral sphincter and/or the external anal sphincter. In some embodiments, operation of the implantable device is controlled in response to sensed information of the patient.
[034] With reference to the greatly simplified view of FIG. 1 , the human pelvic region includes a bladder 10 and a rectum 12. Contents of the bladder 10 are evacuated through a urethra 14, whereas contents of the rectum 12 are evacuated through the anus 16. Pelvic floor muscles 18 support the pelvic organs and span the bottom of the pelvis. The pelvic floor muscle layer 18 has holes for passage of the urethra 14 and the anus 16, and normally wraps quite firmly around these holes to help keep the passages shut.
[035] With additional references to the greatly simplified view of FIG. 2, the bladder 10 is a hollow muscular organ connected to the kidneys by the ureters. The detrusor 30 muscle (referenced generally) is smooth muscle found in the wall of the bladder 10. The urethra 14 is a tube or duct by which urine is conveyed out of the body from the bladder 10. Internal and external sphincters control flow of urine through the urethra 14; under normal conditions, when either of these muscles contracts, the urethra 14 is sealed shut. In particular, an internal urethral sphincter (IUS) 32 (referenced generally) is a smooth muscle that constricts the internal orifice of the urethra 14. The IUS 32 is located at the junction of the urethra 14 with the bladder 10 and is continuous with the detrusor muscle 30, but is anatomically and functionally fully independent from the detrusor muscle 30. An external urethral sphincter (EUS) 34 is located in the deep perineal pouch, at the mid urethra in females and inferior to the prostate in males. Urine is excreted from the kidneys and stored in the bladder 10 before elimination via the urethra 14 during what is known as the micturition reflex. During periods of bladder filling, the storage of urine is promoted by the actions of the internal and external urethral sphincters 32, 34 and the pelvic floor musculature 18. During micturition, these sphincters 32, 34 relax and the smooth muscle of the bladder (the detrusor muscle 30) contracts, resulting in the expulsion of urine.
[036] The body of the bladder 10 is directly innervated by efferent fibers that arise from parasympathetic postganglionic neurons in the pelvic ganglia and intramural ganglia and by efferent fibers that arise from sympathetic postganglionic neurons in the lumbosacral sympathetic chain and hypogastric ganglia/pelvic ganglia. This is generally reflected in FIG. 2 by reference to a pelvic nerve 40 and a hypogastric nerve 42. The internal urethral sphincter 32 receives innervation from the hypogastric nerve 42. The external urethral sphincter 34 is directly innervated by motor neurons in the sacral segments of the spinal cord via the pudendal nerve 44.
[037] Urinary continence is generally defined as the act of storing urine in the bladder 10 until the bladder 10 can be appropriately evacuated. Urinary continence requires control of the detrusor muscle 30 and is the result of complex coordination between multiple centers in the brain, brain stem, spinal cord, and peripheral nerves. As described above, micturition is a coordinated act of bladder elimination that involves relaxing the pelvic floor muscles 18, contracting the detrusor muscle 30, and simultaneously opening the urethral sphincters 32, 34 to achieve complete emptying of the bladder. Stress incontinence can be defined as the involuntary leakage of urine from the bladder 10 accompanying physical activity (e.g., laughing, coughing, sneezing, etc.) which places increased pressure on the abdomen. The leakage occurs even though the bladder muscles (detrusor muscle 30) is not contracting and an urge to urinate is not present. Stress incontinence can develop when the urethral sphincters 32, 34, the pelvic floor muscles 18, or all of these structures have been weakened or damaged and cannot dependably hold in urine. With urethral hypermobility, the bladder 10 and urethra 14 shift downward when abdominal pressure rises, and there is no hammock-like support for the urethra 14 to be compressed against to keep it closed as in the normal, non-disordered case. With urethral incompetence, problems in the urinary sphincter 32, 34 keep it from closing fully or allow it to pop open under pressure. Urinary urge incontinence (“UUI”) (sometimes referred to as overactive bladder (“OAB”) or detrusor overactivity) entails the involuntary leakage of urine from the bladder 10 when a sudden strong need to urinate is felt. There is a sudden involuntary contraction of the muscular wall (the detrusor 30) of the bladder that signals an immediate need to urinate, which can happen even when the bladder 10 is not full. Mixed incontinence is the term used to describe a combination of both overactive bladder and stress incontinence. [038] Internal and external sphincters are similarly utilized to control the flow of fecal matter and gas through the anus 16 (i.e., the internal anal sphincter and the external anal sphincter), acting to keep the anal canal and orifice closed. Action of the internal anal sphincter (IAS) is entirely involuntary, and it is in a state of continuous maximal contraction. The external anal sphincter (EAS) is always in a state of contraction, but can be voluntarily put into a condition of greater contraction so as to more firmly occlude the anal orifice. Similar to urinary continence, bowel continence is the act of storing feces until an acceptable time and opportunity for elimination. Bowel continence requires competent internal and external sphincters, pelvic floor musculature, and intact neurological pathways. Neurological control of bowel continence is complex and requires coordinated reflex activities from the autonomic and enteric nervous systems. The colon can be visualized as a closed, pliant tube bounded by the ileocecal valve and the anal sphincters. The continuous, smooth muscle layer at the end of the rectum 12 thickens to form the internal anal sphincter (IAS); the external anal sphincter (EAS) is a circular band of striated muscle that contracts with the pelvic floor. Parasympathetic innervation of the IAS from the pelvic plexus originates from the sacral cord (S1 to S2). Sympathetic stimulation of the IAS causes contraction. The EAS is composed of both smooth and striated muscle. The smooth muscle of the EAS is innervated by the enteric nervous system. The striated component of the EAS is innervated by the pudendal nerve that exits the cord at sacral levels S2, S3, and S4.
[039] Fecal incontinence can be defined as the involuntary loss of rectal contents (feces, gas) through the anal canal and the inability to postpone an evacuation until socially convenient. For example, injuries to one or both of the EAS and IAS may make it difficult to hold stool back properly. Injury to the nerves that sense stool in the rectum or those that control the anal sphincter can also lead to fecal incontinence. A generalized weakness of the pelvic floor 18 can lead to an impaired barrier to stool in the rectum 12 entering the anal canal, and this is associated with incontinence to solids. The pelvic floor 18 is innervated by the pudendal nerve and the S3 and S4 branches of the pelvic plexus. If the pelvic floor muscles 18 lose their innervation, they cease to contract and their muscle fibers are in time replaced by fibrous tissue, which is associated with pelvic floor weakness and incontinence.
[040] With the above in mind, various treatment systems and methods have been disclosed that treat bladder and/or bowel dysfunction (e.g., one or more of urinary incontinence, UUI and fecal incontinence) by supplying stimulation signals to an electrode implanted to apply the stimulation signal to one or more nerves and/or muscles of the patient that, for example, influence the behavior of musculature of the pelvic region of the patient, for example musculature relating to one or both of urinary incontinence and fecal incontinence (e.g., the external urethral sphincter 34, the internal urethral sphincter 32, pelvic floor muscles 18, the external anal sphincter, the internal anal sphincter, etc.). Examples of such systems and methods are provided in PCT Publication No. 2020/243104 (Rondoni, et al.) and PCT Publication No. WO 2022/192726 (Rondoni, et al.) the entire teachings of each of which are incorporated herein by reference.
[041] One example of a treatment system 50 for treatment of bladder and/or bowel dysfunction in accordance with principles of the present disclosure is provided in FIG. 3 and includes an implantable medical device (IMD) 60 (referenced generally) and optionally one or more sensors 62 (e.g., one or more of an accelerometer, a pressure sensor, a strain sensor, bioimpedance sensor, electrical voltage sensor, etc.). In general terms, the IMD 60 includes an implantable pulse generator or implantable component of a pulse generator (collectively identified as “IPG”) 64 and one or more stimulation elements (e.g., electrode or electrode assembly) 66. The IPG 64 is configured for implantation into a patient, and is configured to provide and/or assist in the performance of therapy to the patient. With formats in which the IPG 64 is an implantable pulse generator, a power source (e.g., battery) is carried within a housing of the implantable pulse generator and from which stimulation energy is generated. With formats in which the IPG 64 is an implantable component of a pulse generator, the implantable component(s) can include a receiver unit (e.g., receiver coil or similar device) that receives power and/or a signal from an external device (external the patient) that typically would be positioned on top of the skin over the location of the receiver coil. The external device can generate/deliver the stimulation energy at a desired setting (e.g., amplitude, pulse width, frequency, pulse train length, etc.) to be received by the implanted receiver unit and conducted to the stimulation element(s) 66 for activation of tissue. The implanted receiver unit may or may not operate to modify the signal it receives prior to delivery to the stimulation element(s) 66. The external transmitter/controller may receive sensing signals from external sensor, receive sensing signals from one or more implanted portions of the implantable component via telemetry, etc. Unless stated otherwise, reference to “IPG 64” is inclusive of both an implantable pulse generator and an implantable component of a pulse generator as described above. The stimulation element 66 is configured to be implanted proximate a selected segment or region of the patient’s anatomy, and is electrically connected to the IPG 64, for example via a lead. In other embodiments, the IPG 64 and the stimulation element 66 can be provided as components of a single or integral device, such as a microstimulator, as are known in the art. The IPG 64 is programmed to deliver (or is prompted to deliver) stimulation signals to the stimulation element 66 that in turn apply the signal. In some embodiments, the IPG 64 is programmed (or is prompted) to initiate, cease and/or modulate (e.g., titrate) delivered stimulation signals based upon one or more physical parameters of the patient. In this regard, the sensor(s) 62 sense the physical parameter of interest, and provide the so-sensed parameter to the IPG 64 (or other component controlling operation of the IPG 64). The sensor(s) 62 can be carried by the IPG 64, can be connected to the IPG 64, or can be a standalone component not physically connected to the IPG 64. The sensor(s) 62 can be self-contained, and communicate with the IPG 64 in some optional embodiments. In some embodiments, the sensor(s) 62, the IPG 64, and the stimulation element 66 can be provided as components of a single or integral device. In some embodiments, the treatment system 50 can further include an optional external device 68. Where provided, the external device 68 can, in some non-limiting embodiments, wirelessly communicate with the IMD 60. [042] The IPG 64 can assume various forms known in the art for generating a nerve-stimulating signal for delivery to the stimulation element(s) 66. For example, the IPG 64 can include a sealed case or enclosure maintaining a power source (e.g., battery) and electrical/circuitry components appropriate for formatting energy from the power source as the desired stimulation signal (e.g., a nerve-stimulation signal). In some embodiments, the IPG 64 is provided as part of, or is electronically linked to, a control system that includes a control portion 70 providing one example implementation of a control portion forming a part of, implementing, and/or generally managing stimulation element(s), power/control elements (e.g. pulse generators, microstimulators), sensors, and related elements, devices, user interfaces, instructions, information, engines, elements, functions, actions, and/or methods, as described throughout examples of the present disclosure. In some examples, the control portion 70 includes a controller and a memory. In general terms, the controller comprises at least one processor and associated memories. The controller is electrically couplable to, and in communication with, memory to generate control signals to direct operation of at least some of the stimulation elements, power/control elements (e.g., pulse generators, microstimulators) sensors, and related elements, devices, user interfaces, instructions, information, engines, elements, functions, actions, and/or methods, as described throughout examples of the present disclosure. In some non-limiting examples, these generated control signals include, but are not limited to, employing instructions and/or information stored in the memory to at least direct and manage treatment of bladder and/or bowel dysfunction by stimulating nerve(s), nerve branch(es) and/or muscle(s), for example to activate one or more of the external urethral sphincter 34 and the external anal sphincter, and/or pelvic floor nerves (e.g., the pudendal nerve 44, the sacral nerve) to relax the detrusor muscle 30 and prevent or reduce urgency or frequency.
[043] In some instances, the controller or control portion 70 may sometimes be referred to as being programmed to perform the actions, functions, routines, etc. of the present disclosure. In some examples, at least some of the stored instructions are implemented as, or may be referred to as, a care engine, a sensing engine, monitoring engine, and/or treatment engine. In some examples, at least some of the stored instructions and/or information may form at least part of, and/or, may be referred to as a care engine, sensing engine, monitoring engine, and/or treatment engine.
[044] In response to or based upon commands received via a user interface and/or via machine readable instructions, the controller generates control signals as described above in accordance with at least some of the examples of the present disclosure. In some examples, the controller is embodied in a general purpose computing device while in some examples, the controller is incorporated into or associated with at least some of the stimulation elements, power/control elements (e.g. pulse generators, microstimulators), sensors, and related elements, devices, user interfaces, instructions, information, engines, functions, actions, and/or method, etc. as described throughout examples of the present disclosure.
[045] For purposes of the present disclosure, in reference to the controller, the term “processor” shall mean a presently developed or future developed processor (or processing resources) that executes machine readable instructions contained in a memory. In some examples, execution of the machine readable instructions, such as those provided via the memory of the control portion 70 cause the processor to perform the above-identified actions, such as operating the controller to implement the sensing, monitoring, treatment, etc. as generally described in (or consistent with) at least some examples of the present disclosure. The machine readable instructions may be loaded in a random access memory (RAM) for execution by the processor from their stored location in a read only memory (ROM), a mass storage device, or some other persistent storage (e.g., non-transitory tangible medium or non-volatile tangible medium), as represented by the memory. In some examples, the machine readable instructions may comprise a sequence of instructions, a processorexecutable machine learning model, or the like. In some examples, the memory comprises a computer readable tangible medium providing non-volatile storage of the machine readable instructions executable by a process of the controller. In some examples, the computer readable tangible medium may sometimes be referred to as, and/or comprise at least a portion of, a computer program product. In other examples, hard wired circuitry may be used in place of or in combination with machine readable instructions to implement the functions described. For example, the controller may be embodied as part of at least one application-specific integrated circuit (ASIC), at least one field-programmable gate array (FPGA), and/or the like. In at least some examples, the controller is not limited to any specific combination of hardware circuitry and machine readable instructions, nor limited to any particular source for the machine readable instructions executed by the controller.
[046] In some examples, the control portion 70 may be entirely implemented within or by a stand-alone device.
[047] In some examples, the control portion 70 may be partially implemented in the IPG 64 and partially implemented in a computing resource separate from, and independent of, the IPG 64. For instance, in some examples the control portion 70 may be implemented via a server accessible via the cloud and/or other network pathways. In some examples, the control portion 70 may be distributed or apportioned among multiple devices or resources such as among a server, a treatment device (or portion thereof), and/or a user interface.
[048] In some examples, the control portion 70 is entirely implemented within or by the IPG 64 (thereby defining an IPG assembly), which has at least some of substantially the same features and attributes as a pulse generator (e.g., power/control element, microstimulator) as previously described throughout the present disclosure. In some examples, the control portion 70 is entirely implemented within or by a remote control (e.g., a programmer) external to the patient’s body, such as a patient control and/or a physician control (e.g., the external device 68). In some examples, the control portion 70 is partially implemented in the IPG 64 assembly and partially implemented in the remote control (at least one of the patient control and the physician control).
[049] The systems and methods of the present disclosure are in no way limited to a particular stimulation target site(s) or a particular stimulation therapy regimen. The stimulation therapies or algorithms programmed to, or implemented by, the control portion 70 can be of any format deemed useful for the patient being treated, and may or may not act upon information from the sensor(s) 62. With reference between FIGS. 1 -3, the system 50 can be configured and implanted to provide stimulation therapy to one or more nerves and/or muscles that, for example, influence the behavior of musculature of the pelvic region of the patient, for example musculature relating to one or both of urinary incontinence and fecal incontinence (e.g., the external urethral sphincter 34, the internal urethral sphincter 32, pelvic floor muscles 18, the external anal sphincter, the internal anal sphincter, etc.). For example, stimulation can be provided to one or more of the pudendal nerve 44, the pelvic nerve 40, the sacral nerve, hypogastric, or branches thereof. For example, stimulation can be provided to a deep branch of the pudendal nerve 44 or other nerve, for example applied to a distal branch of the pudendal nerve 44 (or other nerve) at or in highly close proximity to a location where the branch contacts or terminates a muscle (or other anatomical feature) of interest. With optional embodiments in which the treatment system 50 is configured and implanted to deliver stimulation to two (or more) target sites (e.g., two or more of the pudendal nerve 44, the pelvic nerve 40, the sacral nerve, the hypogastric nerve, etc., and/or two or more different locations along one incontinence amelioration-related nerve and/or different incontinence amelioration-related nerves, etc.), the so-applied simulation can be toggled (e.g., simultaneous, alternating, overlapping, unilateral, bilateral, selective), optionally while additionally toggling/adjusting one or more stimulation parameters e.g.., amplitude, frequency, pulse width, duty cycle, pulse shape, etc.). Alternatively or in addition, the system 50 can apply electrical stimulation to tissue sites proximate a nerve or nerve branch of interest. In yet other embodiments, stimulation can be applied directly to a muscle. Various, non-limiting examples of stimulation protocols or algorithms are described in PCT Publication No. 2020/243104 (Rondoni, et al.) and PCT Publication No. WO 2022/192726 (Rondoni, et al.) the entire teachings of each of which are incorporated herein by reference.
[050] The stimulation element(s) 66 can assume various forms appropriate for applying electrical stimulation to the anatomical feature (e.g., nerve) of interest, and can be provided as part of, or carried by a lead or lead assembly or the like. The stimulation element(s) 66 can be or include one or more electrodes in the form of ring electrodes, segmented electrodes, partial ring electrodes, electrode arrays, paddle leads, etc. In some examples, the stimulation element(s) may be, include, or be provided a part of a cuff electrode, comprising at least some of substantially the same features and attributes as described in Bonde et al., U.S. Patent No. 8,340,785, Self Expanding Electrode Cuff, issued on December 25, 2012, and Bonde et al., U.S. Patent No. 9,227,053, Self Expanding Electrode Cuff, issued on January 5, 2016, both which are hereby incorporated by reference in their entirety. Moreover, in some examples a stimulation lead, which may comprise one example implementation of a stimulation element, may comprise at least some of substantially the same features and attributes as the stimulation lead described in U.S. Patent No. 6,572,543 to Christopherson et al., and which is incorporated herein by reference in its entirety. Other non-limiting examples of stimulation elements and leads useful with the present disclosure are provided in PCT Publication No. 2020/243104 (Rondoni, et al.) and PCT Publication No. WO 2022/192726 (Rondoni, et al.) the entire teachings of each of which are incorporated herein by reference.
[051] With the above generalities in mind, the lead can be delivered and implanted in various manners to position the stimulation element(s) 66 at an intended target site. Unless stated otherwise, the stimulation element(s) 66 can be delivered to the intended target site via a variety of different surgical techniques as would be apparent to one of ordinary skill (e.g., locating a device carrying the stimulation element(s), such as a lead, cuff electrode, microstimulator, etc.). In yet other embodiments, the stimulation element(s) can be provided as part of a trialing system or testing system that need not necessarily include the sensor(s) 62 or components of the IPG 64.
[052] In some embodiments, systems and methods of the present disclosure relate to programming and/or algorithms useful for controlling or prompting operation of the treatment system 50. Some methods of the present disclosure can include prompting the IMD 60 to initiate delivery of, cease delivery of and/or modulate one or more of the stimulation signals (e.g., via programming provided with the control portion 70) based upon or as a function of sensed patient-related information. In some examples, the stimulation signal is initiated and/or modulated based on sensed patient information. In some examples, one or more of the amplitude, rate, and pulse duration of the stimulation signal is modulated based upon, for example, the sensed patient information. Alternatively or in addition, the duty cycle of the stimulation signal may be altered in response to the sensed patient information.
[053] The programming and/or algorithms of the present disclosure can be utilized alone or in combination with other stimulation delivery triggering, modulation, or other operational control. Thus, the systems and methods of the present disclosure can include any of the control algorithms described herein along with one or more additional automated control algorithms and/or control instructions. In the descriptions below, reference may be made to use or implementation of a “criteria” or “condition”, and comparison of one or more sensor signals to the criteria or condition. Unless otherwise noted, reference to a sensor signal (or parameter thereof) meeting a “criteria” or meeting a “condition” entails the signal containing or being consistent with, or having a goodness of fit measure with, one or more certain features such as an absolute value, a range of waveform shapes or patterns, or, in the case of vectored signals such as acceleration, pointing within a range of directions. Moreover, a sensor “signal” is inclusive of a signal from a single sensor or derived from two or more physical sensors (e.g., difference or commonality between accelerometers in two different locations). In the descriptions below, modulating or selecting a “level of stimulation” is in reference to one, or a combination of two or more of, a stimulation parameter such as frequency, intensity (or amplitude), or other waveform properties, and activated electrode(s) (for implementations in which two or more stimulation delivery electrodes have been implanted) unless otherwise noted.
[054] In some examples, systems and methods of the present disclosure can include operating or executing a triggering-type algorithm formatted to prompt or trigger delivery of stimulation energy to a target site based upon the amplitude of a signal from at least one sensor otherwise sensing patient-related information, for example to prevent a possible leakage event. In some embodiments, the sensed patient-related information is abdominal or intra-abdominal pressure determined from a sensor formatted and located to sense abdominal or intra-abdominal pressure (or a surrogate for abdominal or intra-abdominal pressure), although any other sensed parameter deemed or determined to implicate or predict onset of a possible leakage event (e.g., bladder volume) can be employed. With this in mind, some algorithms of the present disclosure can be formatted to trigger delivery of stimulation energy when an amplitude of the sensor signal exceeds a preset or predetermined threshold value (e.g., an absolute threshold value).
[055] By way of non-limiting example, FIG. 4A plots an amplitude 100 of a signal from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the amplitude meets a criteria, for example exceeds an absolute threshold value 102 (labeled at point 104). Alternatively or in addition, the triggering algorithm can utilize a baseline; when the sensor signal’s amplitude exceeds the baseline by a designated or threshold value (or meets another designated criteria), the algorithm prompts the delivery of stimulation. With these and related embodiments, the threshold value or other criteria can be determined or assigned in various manners, for example as a number of standard deviations above the baseline (e.g., two standard deviations). Likewise, the criteria can include temporal information such as the sensor value remaining above a baseline for a given period of time. There may be multiple thresholds associated with multiple time periods. For example, the criterion might require the sensor value to exceed one threshold for 1 .0 second and a second or a higher threshold of 0.5 second. The baseline amplitude value can be determined or designated in various manners. For example, a running average of the signal amplitude can be established as a baseline value. Thus, a current baseline value can be the average amplitude over an immediately previous length of time (e.g., immediately previous 5 seconds, immediately previous 10 seconds, etc.). FIG. 4B shows the amplitude 100 plot of FIG. 4A along with a running average baseline 106. At time 108, the current amplitude 100 exceeds the baseline 106 by the threshold value (e.g., two standard deviations); under these circumstances, the triggering algorithm is formatted to prompt delivery of stimulation to the patient.
[056] In some examples, systems and methods of the present disclosure can include operating or executing a triggering-type algorithm formatted to prompt or trigger delivery of stimulation energy to a target site based upon a rate of change in the signal from at least one sensor otherwise sensing patient-related information, for example to prevent a possible leakage event. A sudden rate of change can be useful to indicate or predict a leak-causing event (e.g., a sneeze). In some embodiments, the sensed patient-related information is abdominal or intra-abdominal pressure determined from a sensor formatted and located to sense abdominal or intra- abdominal pressure (or a surrogate for abdominal or intra-abdominal pressure), although any other sensed parameter deemed or determined to implicate or predict onset of a possible leakage event (e.g., bladder volume) can be employed. With this in mind, some algorithms of the present disclosure are formatted to trigger delivery of stimulation energy when the sensor signal rate of change exceeds a predetermined or fixed value, or other criteria.
[057] By way of non-limiting example, FIG. 5 plots a rate of change 120 in a signal from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the rate of change meets a criteria, for example exceeds an absolute threshold value 122 (labeled at point 124). Alternatively or in addition, the triggering algorithm can utilize a second derivative (or higher) level sensor signal rate of change as the basis for prompting or triggering stimulation.
[058] In some examples, systems and methods of the present disclosure can include operating or executing a triggering-type algorithm formatted to prompt or trigger delivery of stimulation energy to a target site based upon a combination of the amplitude and rate of change of a signal from at least one sensor otherwise sensing patient-related information. In some embodiments, the triggering algorithm is formatted to trigger simulation when a combination of both an amplitude threshold and a rate of change threshold are simultaneously achieved. For example, FIG. 6 plots an amplitude 130 and a rate of change 132 of a sensor signal over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The triggering algorithm provides an amplitude criteria 140 (e.g., threshold value) and a rate of change criteria 142 (e.g., threshold value). When the amplitude 130 meets the amplitude criteria 140 (e.g., exceeds the threshold) and the rate of change 132 meets the rate of change criteria 142 (e.g., exceeds the threshold), the triggering algorithm prompts or triggers delivery of stimulation 150 to the patient. For example, at time T1 , the amplitude 130 meets the amplitude criteria 140, but the rate of change 132 does not meet the rate of change criteria 142; under these circumstances, the triggering algorithm does not prompt delivery of stimulation at time T1. At time T2, the amplitude 130 meets the amplitude criteria 140, but the rate of change 132 does not meet the rate of change criteria 142. As such, the triggering algorithm does not prompt delivery of stimulation at time T2. At time T3, the rate of change 132 meets the rate of change criteria 142 while the amplitude 130 continues to meet the amplitude criteria 140. Thus, at time T3, the triggering algorithm prompts the delivery of the stimulation 150. Parameters of the so-delivered stimulation can take various forms; for example, the stimulation 150 can have a predetermined level and can be delivered for a predetermined length of time; alternatively, the stimulation 150 can be stopped once the amplitude 130 no longer meets the amplitude criteria 140 or the rate of change 132 no longer meets the rate of change criteria 142. In yet other examples, the predetermined duration of the stimulation 150 can be selectable based upon a severity of the detected event in some embodiments. For example, a higher amplitude of detected intraabdominal pressure may cause the system to select a longer duration. At time T4, the amplitude 130 meets the amplitude criteria 140, but the rate of change 132 does not meet the rate of change criteria 1 2; under these circumstances, the triggering algorithm does not prompt delivery of stimulation at time T4.
[059] In other embodiments, the triggering algorithm can be formatted to prompt delivery of stimulation when the sensor signal amplitude meets designated amplitude criteria (e.g., exceeds an amplitude threshold value), and the amplitude criteria (e.g., amplitude threshold value) is modulated by the sensor signal rate of change. For example, the trigger algorithm can raise or lower the amplitude threshold value when the rate of change rises above a designated value and/or falls below a designated value.
[060] In other embodiments, some algorithms of the present disclosure can dictate or determine a level of stimulation to be delivered to the patient based upon the amplitude and/or rate of change of a signal from at least one sensor otherwise sensing patient-related information. For example, the algorithm can be formatted such that a rapid rate of change in a sensor signal (from a sensor that is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc.) results in an increase in stimulation current amplitude delivered to the patient, presuming the amount of stimulation intensity needed to avoid, prevent or limit leakage goes up with increasing rate of change of pressure or surrogate for pressure. Stated otherwise, some algorithms of the present disclosure operate to change the magnitude of stimulation delivered to the patient based upon the magnitude of the sensor signal, in a manner akin to the trigger algorithms described above.
[061] In some examples, systems and methods of the present disclosure can include operating or executing a triggering-type algorithm formatted to prompt or trigger delivery of stimulation energy to a target site based upon an integral of a signal from at least one sensor otherwise sensing patient-related information over a designated time period. In some embodiments, the sensed patient-related information is abdominal or intra-abdominal pressure determined from a sensor formatted and located to sense abdominal or intra-abdominal pressure (or a surrogate for abdominal or intra-abdominal pressure), although any other sensed parameter deemed or determined to implicate or predict onset of a possible leakage event (e.g., bladder volume) can be employed. With this in mind, some algorithms of the present disclosure include a criteria, for example a threshold value, against which the sensor signal is compared; when the sensor signal meets the criteria (e.g., exceeds the threshold value), stimulation is delivered to the patient. In addition, the algorithm establishes a review window corresponding to the length of time the sensor signal continues to meet the criteria (e.g., remains above the threshold value). The accumulated amount of the sensor signal (or the amount the sensor signal exceeds the threshold value) during the review window is integrated to produce a value, or running value, that is then compared to the threshold value (or other criteria). Where the integral value deviates from the criteria, the criteria (e.g., threshold value) can be adjusted. By way of non-limiting example, for some patients, the integral of pressure over time may be a useful indicator of energy or effort being exerted by the patient. Thus, an elevated pressure integral can represent greater effort being exerted by the patient in advance of, for example, a leakage event and/or other conditions of interest such as fatigue. Under these and other circumstances, some algorithms of the present disclosure can recognize elevated effort (via the pressure integral or other parameter integral) and implement a sensor signal criteria, such as a sensor signal threshold value (e.g., a rate of change threshold value), that will result in stimulation being triggered when the patient subsequently encounters a similar need to exert greater effort.
[062] Returning to FIG. 3, in some embodiments the systems and methods of the present disclosure can include or incorporate one or more stimulation methods or algorithms for prompting the IMD 60 to initiate delivery of, cease delivery of, and/or modulation one or more of the stimulation signals (e.g., via programming provided with the control portion 70) in place of, or in addition to, the methods and algorithms described above. In some embodiments, the algorithms can be acted upon or utilize sensed patient-related information. In some examples, the stimulation signal is initiated and/or modulated based on sensed patient information. In some examples, one or more of the amplitude, rate, and pulse duration of the stimulation signal, or stimulation delivery electrode selection, is modulated based upon, for example, the sensed patient information. Alternatively or in addition, the duty cycle of the stimulation signal is altered in response to the sensed patient information.
[063] In some embodiments, methods and algorithms of the present disclosure can implement two or more sensing signals to determine one or more stimulation delivery parameters (e.g., timing or triggering of stimulation). For example, where two or more sensors (or sensor units) are provided that sense or detect patient-related information and that differ from one another by one or both of format and location, the so-provided sensor information can be considered and acted upon in a complimentary manner by some algorithms of the present disclosure. With these and related embodiments, some algorithms of the present disclosure can leverage a likelihood or ability of certain sensor types and/or locations, in combination, to detect or predict occurrence of leakage-type events or scenarios. FIG. 7 represents one example of a stimulation protocol or algorithm implemented by some embodiments of the present disclosure and responsive to a combination of sensing signals. In particular, FIG. 7 presents a simplified representation of a first signal 200 generated by a first sensor and a simplified representation of a second signal 202 generated by a second sensor. The signals 200, 202 are generated over the same period of time and during which the patient engages in various activities. The first and second sensors can assume various forms and can be located to detect a desired parameter of the patient. In one non-limiting example, the first sensor is an accelerometer located, for example, near the patient’s spine, and the second sensor is an electromyography (“EMG”) sensing unit located, for example, near the patient’s paraspinal muscles or erector spinae. With embodiments in which an accelerometer is utilized as the first sensor, the first signal 200 can be the raw signal, rate of change, frequency, etc. Regardless, with some algorithms of the present disclosure, a first sensor criteria (e.g., threshold value or level) 210 is established with respect to the first sensor signal 200. Similarly, a second sensor criteria (e.g., threshold value or level) 212 is established with respect to the second sensor signal 202.
[064J With some algorithms of the present disclosure, the first and second sensor signals 200, 202 and corresponding criteria 210, 212 are reviewed or considered in tandem. In some examples, the algorithm can be programmed to consider the first and second sensor signals 200, 202 in sequence, operating to trigger or prompt the delivery of stimulation (or increasing a level of currently delivered stimulation) to the patient only after a preset order where the first signal 200 is determined to meet the first sensor criteria 210 (e.g., exceeds the first sensor threshold) and then determining that the second sensor signal 212 meets the second sensor criteria 212 (e.g., exceeds the second sensor threshold). Thus, for example, at time TI , the first sensor signal 200 meets the first sensor criteria 210, but the second sensor signal 202 does not meet the second sensor criteria 212. Under these circumstances, the algorithm would not prompt the delivery of stimulation (or increase a level of currently delivered stimulation). At time T2, the second sensor signal 202 meets the second sensor criteria 212, but the first sensor signal 200 does not meet the first sensor criteria 210. Again, under these circumstances, the algorithm would not prompt the delivery of stimulation (or increase a level of currently delivered stimulation). At time T3, the first sensor signal 200 meets the first sensor criteria 210, and the second sensor signal 202 is found to meet the second sensor criteria 212. Under these circumstances, the algorithm would prompt the delivery of stimulation 220 (or increase a level of currently delivered stimulation). Parameters of the so-delivered stimulation can take various forms; for example, the stimulation 220 can have a predetermined level and can be delivered for a predetermined length of time; alternatively, the stimulation 220 can be stopped once the first sensor signal 200 no longer meets the first sensor criteria 210 or the second sensor signal 202 no longer meets the rate of second sensor criteria 212. Likewise, parameters of the stimulation could be modulated by a function of one or more sensor signals to allow it to adapt to changing conditions. [065] The methods and algorithms implicated by FIG. 7 are well-suited for sensor implementations that might otherwise give rise to less than optimal leakage event predictions such as where the first sensor is an accelerometer, and the second sensor is an EMG sensing unit. With this non-limiting example configuration, a heightened acceleration event alone without referencing the EMG signal elevation may represent the patient riding in a car or other activity that does not result in intraabdominal pressure sufficient to produce an Sill leak event. Conversely, EMG events alone may not have adequate specificity to predict a leakage event. However, identifying an increased acceleration event in combination with elevated EMG could be useful for predicting intraabdominal events that result from external forces such as running or jumping.
[066] In other embodiments, two (or more) sensor signal sequencing methods and algorithms of the present disclosure can include triggering stimulation (or increasing a level of currently delivered stimulation) only after a preset order in which the first sensor signal meets designated criteria (e.g., exceeds a threshold) followed by the second sensor signal meeting designated criteria (e.g., exceeding a threshold) after a designated period of time. For example, FIG. 8 presents a simplified representation of a first signal 250 generated by a first sensor and a simplified representation of a second signal 252 generated by a second sensor. The signals 250, 252 are generated over the same period of time and during which the patient engages in various activities. The first and second sensors can assume various forms and can be located to detect a desired parameter of the patient. In one non-limiting example, the first sensor is an accelerometer located, for example, near the patient’s spine, and the second sensor is an EMG sensing unit located, for example, in the patient’s back (e.g., erector spinae or latissimus dorsi). With embodiments in which an accelerometer is utilized as the first sensor, the first signal 250 can be the raw signal, rate of change, frequency, etc. Regardless, with some algorithms of the present disclosure, a first sensor criteria 260 (e.g., threshold value or level) is established with respect to the first sensor signal 250. Similarly, a second sensor criteria 262 (e.g., threshold value or level) is established with respect to the second sensor signal 252.
[067] With some algorithms of the present disclosure, the first and second sensor signals 250, 252 and corresponding criteria 260, 262 are reviewed or considered in tandem. In some examples, the algorithm can be programmed to consider the first and second sensor signals 250, 252 in sequence, operating to trigger or prompt the delivery of stimulation (or increasing a level of currently delivered stimulation) to the patient only after a preset order where the first sensor signal 250 is determined to be meeting the first sensor criteria 260 and then, within a designated time window (e.g., 1 second, 5 seconds, 10 seconds, etc.), determining that the second sensor signal 252 is meeting the second sensor criteria 262. Thus, for example, at time T1 , the first sensor signal 250 meets the first sensor criteria 260 to initiate the start of a time window W; during the time window W, the second sensor signal 252 does not meet the second sensor criteria 262. Under these circumstances, the algorithm would not prompt the delivery of stimulation (or increase a level of currently delivered stimulation). At time T2, the first sensor signal 250 meets the first sensor criteria 260 to initiate the start of the time window W; during the time window W, the second sensor signal 252 is found to meet the second sensor criteria 262 at time T3. Under these circumstances, the algorithm would prompt the delivery of stimulation 270 (or increase a level of currently delivered stimulation) at time T3. Parameters of the so- delivered stimulation can take various forms; for example, the stimulation 270 can have a predetermined level and can be delivered for a predetermined length of time; alternatively, the stimulation 270 can be stopped once the first sensor signal 250 no longer meets the first sensor criteria 260 or the second sensor signal 252 no longer meets the second sensor criteria 262.
[068] In other embodiments implicated by the methods and algorithms of FIG. 8, the second sensor signal 252 can instead be an iteration of the first sensor signal 250. For example, the first sensor signal 250 can be a signal from an accelerometer, and the second sensor signal 252 can be a rate of change, frequency, or directionality of the accelerometer signal, or a patient posture/classification as implicated by the accelerometer signal. With these and related embodiments, when the accelerometer signal meets designated criteria (e.g., exceeds a threshold value), a time window is initiated. If the rate of change (or other parameter) of the accelerometer signal meets designated criteria (e.g., exceeds a threshold value) during the time window, the algorithm triggers the delivery of stimulation (or increases a level of currently delivered stimulation). This approach can be useful, for example, to trigger stimulation when the patient engages in more strenuous exercising or running.
[069] In other embodiments, two (or more) sensor signal sequencing methods and algorithms of the present disclosure can include identifying a refractory state via a first sensor signal to prevent stimulation when it would be counterproductive or unnecessary. Under these circumstances, some methods and algorithms can include establishing a refractory state time period and, during the refractory state time period, not triggering stimulation (or increasing a level of currently delivered stimulation) even if the second sensor signal meets corresponding criteria (e.g., a threshold value). In other words, the first sensor signal may reach a level that indicates that the second sensor signal is no longer indicative of an event that can cause leakage, placing the treatment system into a refractory state for a period of time. By way of example, the first sensor can be an EMG sensor unit associated with the pelvic muscle(s), abdominal muscle(s), back muscle(s), or a set of muscles, and the second sensor be arranged to sense abdominal pressure (e.g., an abdominal wall EMG); where the first sensor indicates that adequate bladder/urethral support has been provided, other elevated signals can be ignored (e.g., the abdominal wall EMG) since a urinary leak is unlikely to occur under these circumstances.
[070] In other embodiments, stimulation control methods and algorithms of the present disclosure utilizing information from two (or more) sensors (that are otherwise formatted and positioned to sense or detect patient-related information), can include selecting, setting or adjusting a triggering criteria (e.g., threshold value) for a first one of the sensor signals based upon information implicated by a second one of the sensors. For example, a primary sensor signal criteria (e.g., threshold value) can be selected or adjusted based upon a current body position or posture of the patient that is otherwise determined from a secondary sensor.
[071J For example, FIG. 9 presents a simplified representation of a first signal 300 generated by a first sensor otherwise formatted and located to implicate pressure of the patient (e.g., bladder pressure, abdominal pressure, etc.) over time, and a simplified representation of a sensed body position or posture 302 of the patient over time, contemporaneous with the monitored pressure. The sensed body position can, for example, be generated or implicated by information from an accelerometer or the like and based upon which a determination can be made as to a likely posture of the patient (e.g., in the plot of FIG. 9, the designation “2” is assigned to a determination that the patient is likely upright or standing, the designation “1” is assigned to a determination that the patient is likely sitting, and the designation “0” is assigned to a determination that the patient is likely prone or lying down). With some methods and algorithms of the present disclosure, the first or pressure signal 300 can be compared to criteria (e.g., a threshold value); when the pressure signal 300 meets the criteria (e.g., exceeds the threshold value), the algorithm prompts delivery of stimulation (or alters currently provided stimulation, for example altering one or more of pulse width, frequency, amplitude, etc., of stimulation currently being provided) to the patient. With these and related embodiments, the criteria employed is selected or adjusted as function of the determined body position. Thus, when the determined body position is upright or standing (i.e., “2”), a first criteria 310 is employed; when the determined body position is sitting (i.e., “1”), a second criteria 312 is employed; when the determined body position is supine (i.e., “0”), a third criteria 314 is employed. The criteria 310-314 can each be selected in accordance with a likelihood the patient will experience a leakage event and/or needs continence support. For example, when the patient is upright, the algorithm’s sensitivity is increased as compared to the supine position.
[072] As an alternative to, or in addition to, body position, criteria (e.g., threshold) sensitivity or values can be selected with reference to levels of patient movement. In related embodiments, the methods and algorithms of the present disclosure can be formatted to consider a combination of body position and patient movement for stimulation delivery. For example, the information generated at time T 1 in FIG. 9 can be indicative of the patient sitting up from a supine position. Under these circumstances, the rectus abdominus muscles are recruited (resulting in an elevated pressure), but not necessarily indicative of high intraabdominal pressure or a risk of leakage. When this body position and movement are detected, the trigger based on the pressure signal 300 meeting the second criteria 312 can be suppressed (and/or the second criteria 312 can be reduced in sensitivity).
[073] In other embodiments, stimulation control methods and algorithms of the present disclosure utilizing information from two (or more) sensors (that are otherwise formatted and located to sense or detect patient-related information implicating a potential leakage event) can include implementing or selecting an elevated stimulation level (e.g., increased intensity) when all sensors being monitored reach the corresponding criteria (e.g., threshold value). For example, FIG. 10 presents a simplified representation of a first signal 350 generated by a first sensor and a simplified representation of a second signal 352 generated by a second sensor. The signals 350, 352 are generated over the same period of time and during which the patient engages in various events. The first and second sensors can assume various forms and can be located to detect a desired parameter of the patient. In one non-limiting example, the first sensor can be formatted and positioned to sense a parameter indicative of intrabdominal pressure, and the second sensor is an accelerometer. Regardless, a first sensor criteria 360 (e.g., threshold value or level) is established with respect to the first sensor signal 350. Similarly, a second sensor criteria 362 (e.g., threshold value or level) is established with respect to the second sensor signal 352. With some algorithms of the present disclosure, stimulation to the patient is triggered at a first level 370 when the first sensor signal 350 meets the first sensor criteria 360 (e.g., exceeds the threshold value) or when the second sensor signal 352 meets the second sensor criteria 362 (e.g., exceeds the threshold value). When both of the sensor signals 350, 352 meet the corresponding criteria 360, 362 at the same time, stimulation to the patient is triggered at a second level 372 that is otherwise greater than the first level 370. In some embodiments, the stimulation level when only the first sensor signal 350 is meeting the first sensor criteria 360 can differ from the stimulation level when only the second sensor signal 352 is meeting the second sensor criteria 362. Regardless, an elevated stimulation level is implemented when both of the sensor signals 350, 352 meet the corresponding criteria 360, 362 at the same time. In related embodiments, three or more sensor signals and corresponding criteria (e.g., threshold values) can be monitored or considered. By combining information from two (or more) sensor sources, as evidence of a leak event increases, the stimulation delivered to the patient also increases. In some examples, the multiple signals can be derived from the same sensor (e.g., acceleration values in various directions).
[074] In other embodiments, stimulation control methods and algorithms of the present disclosure are formatted to modulate a level of stimulation delivered to the patient based upon a magnitude of a monitored patient parameter relative to a criteria (e.g., threshold value). By way of non-limiting example, FIG. 11A is a simplified representation of a signal 400 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation at a first level 420 (e.g., intensity) when the sensor signal 400 meets a first criteria 410 (e.g., exceeds a first threshold value), and a second, elevated level 422 when the sensor signal 400 meets a second criteria 412 (e.g., exceeds a second threshold value). With these and related embodiments, detection with higher levels of pressure, EMG or other parameter results in an increased level of stimulation as compared to the nominal stimulation levels delivered when more typical sensing signal levels are detected.
[075] Alternatively or in addition, some triggering algorithms of the present disclosure are formatted to modulate a level of stimulation delivered to the patient based upon a duration that a monitored patient parameter remains in an elevated state relative to a criteria (e.g., threshold value). For example, FIG. 11 B is a simplified representation of a signal 450 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. In some embodiments, the triggering algorithm includes or establishes a criteria 460 (e.g., threshold value) and a duration window W, and is formatted to prompt the delivery of stimulation at a first level 470 when the sensor signal 450 initially meets the criteria 460 (e.g., exceeds the threshold value). Once stimulation is initiated, the triggering algorithm monitors the length of time the sensor signal 450 continues to meet the criteria 460 (e.g., remains above the threshold value). If the sensor signal 450 continues to meet the criteria 460 for a length of time greater than the duration window W, the triggering algorithm prompts an increase in the level of the stimulation being delivered to the patient to a second level 472 (e.g., intensity or amplitude is increased). Thus, detection with longer duration of elevated pressure, EMG or other parameter results in an increased stimulation level. For example, at time T1 , the sensor signal 450 meets the criteria 460; in response, the triggering algorithm prompts delivery of stimulation at the first level 470 and starts the duration window W. The sensor signal 470 no longer meets the criteria 460 at time T2, such that the sensor signal 470 does not continuously meet the criteria 460 for a length of time of the duration window W (that otherwise began at time T1 ); under these circumstances, the triggering algorithm does not increase a level of the stimulation delivered to the patient. At time T3, the sensor signal 450 meets the criteria 460; in response, the triggering algorithm prompts delivery of stimulation at the first level 470 and starts the duration window W. The so-initiated duration window W ends at time T4; at time T4 (and throughout an entirety of the duration window W), the sensor signal 450 meets the criteria 460. Under these circumstances, the triggering algorithm prompts an increase in a level of the stimulation being delivered to the patient to the second level 472 at time T4 (e.g., intensity or amplitude is increased).
[076] Any of the methods and algorithms of the present disclosure, for example any of the triggering-type methods and algorithms, can optionally be adaptively formatted, implementing one or more adjustments over time. In some embodiments, a triggering-type method or algorithm formatted to prompt the IMD 60 (FIG. 3) to initiate delivery of stimulation in response to at least one sensed parameter of the patient meeting a designated criteria will effect changes or adjustments to the criteria under certain circumstances. For example, the threshold value (or other criteria) can be automatically reduced (e.g., sensitivity for prompting stimulation increases) when one or more leaks are reported (e.g., as observed by the patient, clinician, third party, etc.) or detected (e.g., a leak detector worn by the patient). In some non-limiting examples, a leak observed by the patient can be reported to the control portion 70 (FIG. 3) via the external device 68 (FIG. 3). Under these and similar circumstances, the triggering algorithm can shift to greater or lesser sensitivity due to the observed/reported leaks.
[077] By way of non-limiting example, FIG. 12 presents a simplified representation of a sensor signal 500 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The sensor signal 500 can be the raw sensor data, or can be information derived from the raw sensor data (e.g., rate of change in the sensor signal). In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the sensor signal 500 meets a criteria (e.g., exceeds a threshold value). Moreover, with the arrangement of FIG. 12, the patient is afforded the ability to record observed leak events, for example via the external device (FIG. 3) and/or is carrying or wearing a leak detection sensor; instances of recorded leaks over the same time period as the sensor signal 500 are shown in FIG. 12. At time TO, an initial criteria 510 (e.g., threshold value) is established. Later, at time T1 , the sensor signal 500 approaches, but does not meet, the initial criteria 510. Thus, the algorithm does not prompt delivery of stimulation. However, the patient reports (or a sensor detects) an observed leak that occurred at time T1. At time T2, the sensor signal 500 meets the initial criteria 510; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined intensity for a predetermined length of time). At times T3 and T4, the sensor signal 500 again approaches, but does not meet, the initial criteria 510, and the algorithm does not prompt delivery of stimulation. In addition, patient-observed leakage events at times T3 and T4 have been recorded. The algorithm is formatted to consider the recorded leakage events. In view of one or more of the recorded leakage events at times T 1 , T3, and T4, the algorithm is programmed to adjust or adapt the triggering criteria from the initial criteria 510 to an adjusted criteria 512 (e.g., an adjusted threshold value) shortly after the recorded leakage event at time T4, with the adjusted criteria 512 selected to better assist the patient with potential leakage events in the future. In this one example, in that the algorithm is formatted to trigger stimulation when the sensor signal 500 exceeds a threshold value, the adjusted criteria 512 provides a threshold value that is lower than a threshold value of the initial criteria 510, meaning that following the adjustment, the algorithm is now more “sensitive” to changes in the sensor signal 500. At time T5, the sensor signal 500 meets the adjusted criteria 512; in response, the algorithm prompts or triggers the delivery of stimulation to the patient. Notably, a level of the sensor signal 500 at time T5 does not meet the initial criteria 510; had the threshold value not been reduced to the adjusted criteria 512, stimulation would not have been triggered, perhaps leading to another leakage event.
[078] Various methodologies can be utilized to formulate the extent to which the triggering criteria value is adjusted in response to one or more reported leak events as observed by the patient, clinician or third party, or detected by a sensor (e.g., from the initial criteria 510 to the adjusted criteria 512). In some embodiments, rules can be employed, for example lowering (or raising) the threshold value (or other criteria) by a predetermined percentage or absolute value once a certain number of leakage events have been recorded over a predetermined length of time. In other embodiments, the algorithm can be programmed to identify possible correlations between recorded leak events and corresponding sensor signal values, and effect adjustments to the criteria (e.g., threshold value) based upon an identified correlation having a designated level of confidence. In the non-limiting example of FIG. 12, it can be observed that during the leakage events at times T1 , T3, and T4, the sensor signal 500 attained a level designated as L1 . Based upon this information, it can be determined with a reasonable level of confidence that the sensor level L1 implicates a likely leakage event; as a result, the adjusted criteria 512 can be selected to be or approximate level L1. Other adjustment factors or methodologies are also acceptable. The algorithms of the present disclosure can optionally be formatted to shift to a greater or lesser sensitivity due to observed/reported leaks in various fashions, and in some embodiments can be posture specific.
[079] In other embodiments, methods and algorithms of the present disclosure can be formatted to adapt or adjust a triggering criteria (e.g., threshold value) based upon occurrences of unwanted stimulation delivery as reported by the patient to the control portion 70 (FIG. 3), for example via the external device 68 (FIG. 3). Under these and similar circumstances, the triggering algorithm can shift to a decreased sensitivity, thus decreasing the likelihood of stimulation being delivered.
[080] By way of non-limiting example, FIG. 13 presents a simplified representation of a sensor signal 550 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The sensor signal 550 can be the raw sensor data, or can be information derived from the raw sensor data (e.g., rate of change in the sensor signal). In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the sensor signal 550 meets a designated criteria (e.g., exceeds a threshold value). Moreover, with the arrangement of FIG. 13, the patient is afforded the ability to record unwanted stimulation; instances of recorded unwanted stimulation over the same time period as the sensor signal 550 are shown in FIG. 13. At time TO, an initial criteria 560 (e.g., threshold value) is established. Later, at time T1 , the sensor signal 550 meets the initial criteria 560; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined intensity for a predetermined length of time). In addition, the patient reports that the stimulation delivered at time T 1 was unwanted. At time T2, the sensor signal 550 meets the initial criteria 560 and the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined intensity for a predetermined length of time). The patient does not report the stimulation at time T2 as being unwanted. At times T3 and T4, the sensor signal 550 again meets the initial criteria 560, and the algorithm prompts delivery of stimulation. In addition, the patient reports that the stimulation delivered at times T3 and T4 was unwanted. The algorithm is formatted to consider the unwanted delivery of stimulation as reported by the patient. In view of one or more of the recorded unwanted stimulation deliveries at times T1 , T3, and T4, the algorithm is programmed to adjust or adapt the triggering criteria (e.g., threshold value) from the initial criteria 560 to an adjusted criteria 562 shortly after the recorded unwanted stimulation event at time T4, with the adjusted criteria 562 selected to better avoid unwanted stimulation being delivered to the patient in the future. In this one example, in that the algorithm is formatted to trigger stimulation when the sensor signal 550 exceeds a threshold value, the adjusted criteria 562 entails a threshold value that is higher than the threshold of the initial criteria 560, meaning that following the adjustment, the algorithm is now less “sensitive” to changes in the sensor signal 550. At time T5, the sensor signal 560 does not meet the adjusted criteria 562 and delivery of stimulation is not prompted. Notably, a level of the sensor signal 550 at time T5 does meet the initial criteria 560; had the threshold value of the initial criteria 560 not been reduced to the threshold value of the adjusted criteria 562, stimulation would have been triggered at time T5 and this stimulation perhaps would have been unwanted by the patient.
[081] Various methodologies can be utilized to formulate the extent to which triggering criteria (e.g., threshold value) is adjusted in response to one or more patient-reported unwanted wanted stimulation events (e.g., from the initial criteria 560 to the adjusted criteria 562). In some embodiments, rules can be employed, for example raising the threshold value (or other criteria) by a predetermined percentage or absolute value once a certain number of unwanted stimulation events have been recorded over a predetermined length of time. In other embodiments, the algorithm can be programmed to identify possible correlations between recorded unwanted stimulation events and corresponding sensor signal values, and effect adjustments to the threshold value (or other criteria) based upon an identified correlation having a designated level of confidence. In the non-limiting example of FIG. 13, it can be observed that during the unwanted stimulation events at times T1 , T3, and T4, the sensor signal 550 attained a level designated as L1. It can further be observed that the patient did not deem the stimulation delivered at time T2 as being unwanted, and the sensor signal at time T2 attained a level designated as L2. Based upon this information, it can be determined with a reasonable level of confidence that the sensor level L2 implicates a likely leakage event or other circumstances in which stimulation is desired by the patient, but that sensor level L1 does not. As a result, the adjusted criteria 562 can be selected to be or approximate level L2. Other adjustment factors or methodologies are also acceptable.
[082] In other embodiments, methods and algorithms of the present disclosure can be formatted to adapt or adjust a triggering criteria (e.g., threshold value) based upon the number of times stimulation is triggered or delivered over a period of time. For example, the algorithm can be formatted to increase a threshold value (or adjust other triggering criteria formats in a manner that reduces triggering sensitivity and thus decreases a likelihood of stimulation being triggered) when the frequency of triggered stimulation over a period of time exceeds a specific level that is at least one of set by the clinician, a preset value, or as set/adjusted by the patient. Various methodologies can be utilized to formulate the extent to which the triggering criteria is adjusted in response to an elevated frequency of triggered stimulation. In some embodiments, rules can be employed, for example lowering a threshold value by a predetermined percentage or absolute value once a certain number of stimulation events have been recorded over a predetermined length of time, or as requested by the clinician or patient. In other embodiments, the algorithm can be programmed to identify possible correlations between stimulation events and corresponding sensor signal values, and effect adjustments to the triggering criteria based upon an identified correlation having a designated level of confidence. For example, the sensor(s) could include external leak detection; when a patient indicates that a leak occurred, the algorithm automatically references the external leak detection sensor signals at the corresponding point in time. In other examples, the implanted sensors could include an accelerometer and EMG; these and other sensor signal values could automatically be referenced in validating the frequency and total number of stimulation events.
[083] In yet other examples, machine learning can be utilized to adjust triggering criteria and/or stimulation parameter adjustments.
[084] In other embodiments, methods and algorithms of the present disclosure can be formatted to provide a patient the ability to effect (directly or indirectly) adjustments in one or more of triggering sensitivity, configuration or strategy. As a point of reference, for many patients, there are circumstances or situations in which leaks are less acceptable than others. With this in mind, some methods and algorithms of the present disclosure are formatted to consider a level of security from leaks that is desired by the patient and to use this level of security to alter or set the event detection and stimulation production algorithms to be more or less aggressive in assisting in the stopping of leaks.
[085] By way of non-limiting example, FIG. 14 presents a simplified representation of a sensor signal 600 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The sensor signal 600 can be the raw sensor data, or can be information derived from the raw sensor data (e.g., rate of change in the sensor signal). In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the sensor signal 600 meets a designated criteria (e.g., exceeds a threshold value). Moreover, with the arrangement of FIG. 14, information characterizing a desired level of security from leaks is available. With the example of FIG. 14, security levels of “0”, “1”, and “2” are available, with “2” being a higher level of security, “1” an intermediate level of security, and “0” a lower level of security. As described in greater detail below, the level of security setting can be directly selected by the patient in various manners and/or can be determined from sensor data or other factors. While three security levels are shown, in other embodiments, a greater or lesser number can be available.
[086] At time TO, the higher level of security (“2”) has been selected; in response, the algorithm establishes or sets a first criteria 610 (e.g., threshold value) that corresponds with the higher level of security selection. Later, at times T1 and T2, the sensor signal 600 meets the first criteria 610; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined intensity for a predetermined length of time). At time T3, the security level is changed to the intermediate level of security (“1”). In response the algorithm adjusts the triggering criteria to a second criteria 612 that corresponds with the intermediate level of security selection. With the non-limiting example of FIG. 14, the triggering criteria is a threshold (it being understood that other triggering criteria parameters are equally useful); the threshold value of the first criteria 610 is higher than the threshold value of the second criteria 612. At time T4, the sensor signal 600 approaches, but does not meet, the second criteria 612, and the algorithm does not prompt delivery of stimulation. Notably, the sensor value at time T4 does meet the first criteria 610. If the security setting had not been changed from “2” to “1”, stimulation would have been triggered at time T4. At time T5, the sensor signal 600 meets the second criteria 612; in response, the algorithm prompts or triggers the delivery of stimulation to the patient. At time T6, the security level is changed to the lower level of security (“0”). In response the algorithm adjusts the triggering criteria to a third criteria 614 that corresponds with the lower level of security selection. At time T7, the sensor signal 600 approaches, but does not meet, the third criteria 614, and the algorithm does not prompt delivery of stimulation. Notably, the sensor value at time T7 does meet the first criteria 610 and the second criteria 612. If the security setting had not been changed to “0”, stimulation would have been triggered at time T7.
[087] Various methodologies can be utilized to designate the triggering criteria (e.g., threshold value) utilized with a particular security level. In some embodiments, a specified criteria can be predetermined or assigned to each security level. In other embodiments, rules can be employed when adjusting from one security level to another, for example raising or lowering the threshold value (or other criteria) by a predetermined percentage or absolute value in response to a change in security level. In other embodiments, the algorithm can be programmed to identify or learn correlations between stimulation events and corresponding sensor signal values at different security level setting, and effect adjustments to the triggering criteria based upon an identified correlation at each security level. Other adjustment factors or methodologies are also acceptable.
[088] In addition to, or as an alternative to, selecting or adjusting a triggering criteria, other stimulation parameters or strategies can be adjusted based on a security level setting. For example, in some embodiments, methods and algorithms of the present disclosure can alter or adjust a level of stimulation (e.g., an amplitude (or intensity) of stimulation) to be delivered to the patient in response to a triggering event based upon a security level.
[089] By way of non-limiting example, FIG. 15 presents a simplified representation of a sensor signal 650 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The sensor signal 650 can be the raw sensor data, or can be information derived from the raw sensor data (e.g., rate of change in the sensor signal). In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the sensor signal 650 meets a criteria 660 (e.g., a threshold value). Moreover, with the arrangement of FIG. 15, information characterizing a desired level of security from leaks is available. With the example of FIG. 15, security levels of “0”, “1”, and “2” are available as described above. While three security levels are shown, in other embodiments, a greater or lesser number can be available.
[090] At time TO, the higher level of security (“2”) has been selected; in response, the algorithm establishes or sets a first stimulation level 670 (e.g., stimulation energy amplitude) that corresponds with the higher level of security selection. Later, at times T1 and T2, the sensor signal 650 meets the criteria 660; in response, the algorithm prompts or triggers the delivery of stimulation to the patient at the first level 670 (e.g., for a predetermined length of time). At time T3, the security level is changed to the intermediate level of security (“1”). In response the algorithm adjusts the stimulation level to a second stimulation level 672 that corresponds with the intermediate level of security selection. At times T4 and T5, the sensor signal 650 meets the threshold value 660; in response, the algorithm prompts or triggers the delivery of stimulation to the patient at the second stimulation level 672. Notably, if the security setting had not been changed from “2” to “1”, higher intensity stimulation would have been delivered at times T4 and T5. At time T6, the security level is changed to the lower level of security (“0”). In response the algorithm adjusts the stimulation level to a third stimulation level 674 that corresponds with the lower level of security selection. At time T7, the sensor signal 650 meets the criteria 660; in response, the algorithm prompts or triggers the delivery of stimulation to the patient at the third stimulation level 674. Notably, if the security setting had not been changed from “1” to “0”, higher intensity stimulation would have been delivered at time T7.
[091] Various methodologies can be utilized to designate the stimulation level utilized with a particular security level. In some embodiments, a specified simulation level (e.g., stimulation intensity) can be predetermined or assigned to each security level. In other embodiments, rules can be employed when adjusting from one security level to another, for example raising or lowering the stimulation level by a predetermined percentage or absolute value in response to a change in security level. In other embodiments, the algorithm can be programmed to identify or learn correlations between stimulation level and likelihood of preventing leak, and effect adjustments to the stimulation level associated with each level of security based upon an identified correlation. Other adjustment factors or methodologies are also acceptable.
[092] In some embodiments, the methods and algorithms implicated by FIGS. 14 and 15 can be combined. Regardless, with these and related embodiments, during levels of heightened security, the number of unnecessary stimulation events (or “false positives”) or the level of tolerated stimulation amplitude (or other stimulation level parameters) can be higher in exchange for greater freedom from leaks. For designated or determined situations or scenarios (e.g., as selected by the patient), adjustments to the algorithm are implemented. For example, some activities, such as running, could lead to an excessive number of potential leak event detections causing the system to provide stimulation more frequently or at a higher intensity (or other stimulation level parameter) than desired. In such cases, a patient might determine that it is better to wear a pad to absorb leaks or use some other leak mitigation strategy and set the system to be less aggressive concerning leak detection or prevention. Likewise, there are periods, such as when the patient wishes to void stored urine, when it would be advantageous to avoid leak preventing stimulation. It may therefore be useful to have a void setting that can be activated by the patient.
[093] Selection or implementation of a particular security level with the embodiments of FIGS. 14 and 15 can be provided in various fashions. In some examples, the patient is provided with direct control over, or selection of, a desired security level by means of a setting on a remote control or other controller (e.g., the external device 68 (FIG. 3)). The remote control can be a dedicated device, a mobile communication device (e.g., smart phone) operating a software application, etc. In other examples, an activity status of the patient can be determined or designated through the use of various sensors (e.g., accelerometer). For example, one or more algorithms can be operated by the control portion 70 (FIG. 3) to evaluate activity- related sensor data and determine or designate a current activity status of the patient. With these and related embodiments, the control portion 70 can further be programmed to automatically set or alter the security level based on the activity status and/or other sensor-detected factors such as bladder fullness. Such programming could be explicitly input by the patient or a clinician, or it could be established via machine learning using leak detection feedback. Leak detection feedback could be direct via patient input (e.g., the patient presses a button on a dedicated external device when s/he feels a leak, tap gestures as described below, etc.) or via automated sensors such as a pad instrumented to detect leaks that is worn by the patient during a training period. In other embodiments, machine learning could be accomplished by a remote system, and/or can be based on data unique to the patient or on aggregate data from many patient processed centrally or in the cloud.
[094] The patient training period mentioned above can take various forms, and can generate feedback information useful with a number of programming optimization including security level settings. In some examples, the training period can entail defined training routines performed by the patient, such as squats, coughs, sneeze, etc., during which sensor response/patient responses (e.g., leaks) are used for learning. Stimulation parameters including stimulation duration, stimulation frequency (Hz), pulse width, electrode selection, and/or polarity can be evaluated or considered as part of the patient training period. Machine learning, with the inclusion of a leak sensor, over the course of an initial period of use (e.g., first few weeks of use) and/or periodically as needed can serve to improve efficacy, reduce programming/maintenance burden, etc. In some examples, sensor recordings are utilized from the patient specific leak events that are agnostic to what the specific event might be to identify conditions that result in leak events.
[095] Returning to FIG. 3, in other embodiments, triggering-type methods and algorithms of the present disclosure can include providing the patient with an ability to increment or decrement a sensitivity level of the triggering algorithm (e.g., increment or decrement a currently-applied sensor signal threshold value). In other words, with any triggering-type method and algorithm that functions to prompt stimulation delivery when at least one sensor signal (or parameter of the sensor signal) meets a designated triggering criteria (e.g., exceeds orfalls below a threshold value), with some embodiments of the present disclosure, the patient can request or prompt an immediate change to the triggering criteria, for example to prevent urine leakage. In some examples, the external device 68 can be configured or programmed in a manner that permits the patient, otherwise operating the external device 68, to communicate a desire to increment or decrement sensitivity (e.g., increment or decrement the triggering-type algorithm threshold value) to the control portion 70. In other examples, methods and algorithms of the present disclosure are formatted to allow the patient to increase or decrease sensitivity without requiring use of an external device 68.
[096] For example, the control portion 70 can include or is programmed to include an activation engine that operates in response to information sensed by the sensor 62. With some non-limiting embodiments in which the sensor 62 is or includes an accelerometer, the activation engine can be programmed to operate in response to a “tap gesture” with the patient tapping on her/his body in a region of the implanted sensor 62 in a prescribed fashion a certain number of times within a configurable time period (e.g., two quick taps, followed by a brief pause, followed by two quick taps). The taps and pauses would be detected by the accelerometer sensor 62, and are recognized by the control portion 70 as the patient desiring an increase or decrease in sensitivity of the triggering algorithm. These and other similar techniques would allow the patient, without an extra external device, to quickly activate modes, algorithms, and/or defer stimulation.
[097] In other embodiments, triggering-type methods and algorithms of the present disclosure can include providing the patient with an ability to select from a library of sensitivities to correspond with a specific event. For example, the library could include a sensitivity level for jogging and a sensitivity level for more sedentary activities where a sneeze or cough is the more likely cause of a leak. In this regard, “sensitivity” is in reference to at least one of an algorithm triggering criteria and an algorithm type. In some examples, the external device 68 can be configured or programmed in a manner that permits the patient (or a clinician), otherwise operating the external device 68, to communicate a desired sensitivity level from the library to the control portion 70. In other examples, methods and algorithms of the present disclosure are formatted to allow the patient to increase or decrease sensitivity without requiring use of an external device 68. For example, the patient or clinician could select several different modes as the “take home programs” that could be activated via the “tap gesture” techniques described above. [098] In other embodiments, methods and algorithms of the present disclosure can include providing the patient with an ability to request or prompt delivery of stimulation, for example to prevent urine leakage. In some examples, the external device 68 can be configured or programmed in a manner that permits the patient, otherwise operating the external device 68, to communicate a desire to initiate stimulation to the control portion 70. In other examples, methods and algorithms of the present disclosure are formatted to allow the patient to initiate stimulation delivery without requiring use of an external device 68, such as via the “tap gesture” techniques described above. The taps and pauses would be detected by the accelerometer sensor 62, and are recognized by the control portion 70 as the patient desiring delivery of stimulation. In addition to the tap gesture, related methods and algorithms by which the patient can request or prompt delivery of stimulation without requiring a remote can further include the patient performing a dedicated maneuver that can be recognized by the control portion 70 via sensor-provided information. For example, prior to or following the tap gesture, the patient is required to perform a Valsalva maneuver (bearing down on the abdominal muscles while holding breath) in order for the control portion 70 to recognize that the patient desires stimulation therapy.
[099] In other systems, methods and algorithms of the present disclosure, a voiding state or mode is provided that can be activated by the patient when the patient desires to void. Depending upon the pathology of the patient, the voiding state can prevent delivery of stimulation. For example, the control portion 70 can be programmed, in the voiding state, to suppress or reduce delivery of stimulation energy intended to activate or contract the external urethral sphincter 34. Additionally or alternatively, the voiding state can include stimulation intended to enhance or promote micturition such as repeated, brief bursts of stimulation, potentially on different electrodes and/or different stimulation parameters than those used to prevent leakage. For example, the voiding state can include delivering lower frequency energy to a target site (e.g., the pudendal nerve, the dorsal genital nerve, branches thereof, etc.) in a manner that can benefit voiding. In other examples, the voiding state can include stimulating one or more of the hypogastric nerves at the T or L level or other nerve of the sympathetic nervous system relevant to bladder control and/or anal control (e.g., sympathetic nerves from T11, T12 - L1 , L2) in a manner that suppresses the relevant sympathetic nerve drive to thus encourage the natural micturition reflex (e.g., the body’s natural, unconscious or reflexive control over voiding is suppressed). In optional related embodiments, the systems and methods of the present disclosure can include the control portion 70 being programmed, under circumstances where voiding is desired, to prompt the delivery of stimulation to target nerve(s) responsible for driving voiding such as the detrusor muscle, directly activating those muscle(s) while relaxing those intended to prevent accidental leakage. This optional approach may be beneficial for patients with incomplete control over the pelvic floor, such as patients who are convalescent, have spinal cord injury, are unconscious, etc. Regardless, in some examples, the external device 68 can be configured or programmed in a manner that permits the patient, otherwise operating the external device 68, to communicate a desire to initiate the voiding state to the control portion 70. In other examples, methods and algorithms of the present disclosure are formatted to allow the patient to initiate the voiding state without requiring use of an external device 68, such as via the “tap gesture” techniques described above. The taps and pauses would be detected by the accelerometer sensor 62, and are recognized by the control portion 70 as the patient desiring to void. In yet other examples, the patient can utilize the “voiding state” as described above (during which stimulation is not triggered or delivered) for other situations when stimulation is not desired, such as when catheterizing, during certain activities, etc.
[0100] With embodiments in which a “tap gesture” as described above is utilized as, or as part of, a patient-directed prompt to deliver stimulation, change algorithm sensitivity, and/or select an algorithm sensitivity mode, some methods and algorithms of the present disclosure can further be formatted to confirm a patient’s intent (following a recognized tap gesture) before implementing the action. For example, following a recognized tap gesture, the control portion 70 can be programmed to prompt delivery of a small burst or sequence of stimulation above the sensation threshold of the patient, and then wait for the patient to perform a prescribed action (e.g., tapping the body near the sensor 62) within a predetermined time period (e.g., 10 seconds) to confirm the requested action.
[0101] In other embodiments of the present disclosure, triggering-type algorithms are automatically selected and/or adjusted based upon an activity state of the patient. As a point of reference, certain activities would be expected to exacerbate the likelihood of a leak event. It is also expected that the efficacy of certain leak prediction and prevention strategies would be activity dependent with one strategy being more effective during certain activities and other strategies being more appropriate during other activities. Once a current activity state of the patient has been determined or selected, that state can be used to adjust various parameters within the leak event detection algorithm or to switch between leak event detection strategies.
[0102] By way of non-limiting example, FIG. 16 presents a simplified representation of a sensor signal 700 from a sensor over time, where the sensor is otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc. The sensor signal 700 can be the raw sensor data, or can be information derived from the raw sensor data (e.g., rate of change in the sensor signal). In some embodiments, the triggering algorithm is formatted to prompt the delivery of stimulation when the sensor signal 700 exceeds a threshold value. Moreover, with the arrangement of FIG. 16, information characterizing an activity state of the patient is available. With the example of FIG. 16, activity states of “0”, “1”, and “2” are available, with “2” being a low level of activity (e.g., laying or sitting), “1” an intermediate level of activity (e.g., walking), and “0” a higher level of activity (jogging or running). As described in greater detail below, the activity state can be directly selected by the patient in various manners and/or can be determined from sensor data or other factors. While three activity states are shown, in other embodiments, a greater or lesser number can be available. [0103] At time TO, the low activity state (“2”) has been selected; in response, the algorithm establishes or sets a first triggering criteria 710 (e.g., threshold value) that corresponds with the low activity state. Later, at times T1 and T2, the sensor signal 700 meets the first criteria 710; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined level for a predetermined length of time). At time T3, the activity state is changed to the intermediate activity state (“1”). In response the algorithm adjusts the triggering criteria to a second criteria 712 that corresponds with the intermediate activity state. At time T4, the sensor signal 700 approaches, but does not meet, the second criteria 712, and the algorithm does not prompt delivery of stimulation. Notably, the sensor value at time T4 does meet the first criteria 710. If the activity state had not been changed from “2” to “1”, stimulation would have been triggered at time T4. At time T5, the sensor signal 700 meets the second criteria 712; in response, the algorithm prompts or triggers the delivery of stimulation to the patient. At time T6, the activity state is changed to the higher level of activity (“0”). In response the algorithm adjusts the triggering criteria to a third criteria 714 that corresponds with the higher activity state selection. At time T7, the sensor signal 700 approaches, but does not meet, the third threshold value 714, and the algorithm does not prompt delivery of stimulation. Notably, the sensor value at time T7 does meet the first criteria 710 and the second criteria 712. If the activity state had not been changed to “0”, stimulation would have been triggered at time T7.
[0104] Various methodologies can be utilized to designate the triggering criteria (e.g., threshold value) utilized with a particular activity state. In some embodiments, a specified triggering criteria can be predetermined or assigned to each activity state. In other embodiments, rules can be employed when adjusting from one activity state to another, for example raising or lowering a sensitivity of the triggering criteria (e.g., raising or lowering a threshold value) by a predetermined percentage or absolute value in response to a change in activity state. In other embodiments, the algorithm can be programmed to identify or learn correlations between stimulation events and corresponding sensor signal values at different activity states, and effect adjustments to the triggering criteria based upon an identified correlation at each activity state. Other adjustment factors or methodologies are also acceptable.
[0105] In addition to, or as an alternative to, selecting or adjusting a triggering criteria, other algorithm parameters or strategies can be adjusted based on a determined or selected activity state of the patient. In some embodiments, methods and algorithms of the present disclosure can alter, adjust, or select the process by which the triggering algorithm recognizes or predicts likely occurrence of a leak event (and thus prompts stimulation) based upon an activity state of the patient. By way of background, some triggering-type algorithms of the present disclosure are formatted to review a signal from a sensor otherwise monitoring or sensing a physical parameter of the patient relating to continence, for example pressure (e.g., bladder pressure, abdominal pressure, etc.), volume (e.g., bladder volume), etc.; when the sensor signal (or a property thereof) meets a triggering criteria (e.g., exceeds or drops below a designated threshold value), the triggering algorithm prompts the delivery of stimulation to the patient. With this triggering algorithm format, then, onset of a potential leak event is essentially being predicted via the sensor signal, predicated on an assumption that the sensor type and location is providing reliable leak event-related information. With this in mind, an activity state of the patient can affect an efficacy of the information being signaled by some sensor types/locations as an indicator of a likely leak event. For example, a treatment system of the present disclosure can include an EMG sensing unit located to monitor EMG activity at or along the patient’s back or trunk; the EMG signal is reviewed by a triggering algorithm formatted such that when the EMG sensor signal meets a triggering criteria (e.g., exceeds a threshold value), stimulation delivery is prompted. When the patient is in a low activity state (e.g., standing or sitting quietly), this EMG sensor/location can reliably detect the onset of a potential leak event such as a sneeze or cough because the muscles are activated to brace the trunk in anticipation of increased abdominal pressure. However, if the patient is in a high activity state such as running, those same muscles are active independent of any potential leak event, rendering the EMG sensor/location less reliable in detecting oncoming leak events. With these and other scenarios, some systems, methods and algorithms of the present disclosure are formatted to select or alter the parameters used to review the sensor information in predicting or detecting a likely leak event based on a current activity state of the patient and/or select preferred sensor information based on a current activity state of the patient (e.g., in the above hypothetical arrangement, when the patient is in a high activity state, information from a sensor other than the EMG sensor is selected as the input for detecting a likely leak event or triggering stimulation).
[0106] By way of non-limiting example, FIG. 17 presents a simplified representation of methods and algorithms of the present disclosure utilized with a treatment system arrangement that includes one or more sensors formatted and positioned on the patient to signal information indicative of pressure (e.g., abdominal pressure) and one or more sensors formatted and positioned on the patient to signal information indicative of volume (e.g., bladder volume). With this in mind, FIG. 17 plots a signal 750 from the pressure-related sensor over time, and a signal 752 from the volume- related sensor over the same period of time. A pressure-related criteria 760 (e.g., threshold value) is assigned or generated with respect to the pressure-related sensor signal 750, and a volume-related criteria 762 (e.g., threshold value) is assigned or generated with respect to the volume-related sensor signal 752 (e.g., via various programming and/or algorithms). Moreover, with the arrangement of FIG. 17, information characterizing or indicating an activity state of the patient is available. With the example of FIG. 17, activity states of “0”, “1”, and “2” are available, with “2” being a low level of activity (e.g., laying or sitting), “1” an intermediate level of activity (e.g., walking), and “0” a higher level of activity (jogging or running). As described in greater detail below, the activity state can be directly selected by the patient in various manners and/or can be determined from sensor data or other factors. While three activity states are shown, in other embodiments, a greater or lesser number can be available.
[0107] With the example arrangement of FIG. 17, the control portion 70 (FIG. 3) is programmed to select one of the sensor signals 750, 752 for triggering stimulation based upon the current activity state of the patient. When the patient is in a low or intermediate activity state, the pressure-related sensor signal 750 is selected; when the patient is in a high activity state, the volume-related sensor signal 752 is selected. At time TO, the low activity state (“2”) has been designated. In response, the algorithm designates or selects the pressure-related sensor signal 750 as the triggering input. Later, at time T1 , the pressure-related sensor signal 750 meets the pressure-related criteria 760; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined level for a predetermined length of time). Notably, at time T1 , the volume-related sensor signal 752 does not meet the volume-related criteria 762; but because the patient is in the low activity state (“2”), the volume-related sensor signal 752 is not considered. At time T2, the activity state is changed to the high activity state (“0”). In response, the algorithm designates or selects the volume-related sensor signal 752 as the triggering input. At time T3, the pressure-related signal 750 meets the pressure- related criteria 760. However, because the volume-related sensor signal 752 does not meet the volume-related criteria 762 and serves as the triggering input, stimulation is not delivered to the patient. At time T4, the volume-related sensor signal 752 meets the volume-related criteria 762; in response, the algorithm prompts or triggers the delivery of stimulation to the patient.
[0108] Returning to FIG. 3 and as mentioned above, various techniques can be employed to determine or identify an activity state of the patient. In some examples, the patient can designate or indicate a current or anticipated activity state to the control portion 70. For example, the external device 68 can be formatted (e.g., a dedicated controller, a smart phone software application, etc.) to allow a patient to enter an activity state, with this indicated activity state being communicated to the control portion 70. In other embodiments, the patient can indicate an activity state without requiring use of an external device 68, such as via the “tap gesture” techniques described above. Alternatively or in addition, the control portion 70 can be programmed to determine or designate an activity state with reference to information from one or more sensors associated with the patient. For example, an accelerometer signal, optionally combined with other sensor signals such as EMG and or gyroscopic signals, can be used to detect or designate salient activities or activity states such as running, walking, sitting, laying, etc. A number of other sensorbased techniques can be employed to determine or classify an activity state of the patient.
[0109] In other embodiments of the present disclosure, triggering-type algorithm parameters (e.g., sensing parameters, stimulation parameters, etc.) are automatically selected and/or adjusted based upon information provided by one or more devices or systems apart from the treatment system 50 that otherwise operate to sense or detect information or data regarding conditions that are relevant to providing optional therapy, for example information or data relating to one or more of the patient’s environment, activity level, physiological parameters, location, etc. FIG. 18 presents a summary 790 of some external inputs that may be signaled to, and utilized by programming of, the control portion 70 (FIG. 3). For example, ambient pressure can be derived from a current altitude of the patient, with the altitude in turn being determined by an available GPS system (e.g., provided with the patient’s smart phone). Barometric pressure of the patient’s environment can be provided/determined by a common weather app operating on the patient’s smart phone. Changes in the patient’s weight can be determined by various devices, such as an integrated weight scale, smart mattress, smart chair, etc. Changes in the patient’s water weight can be determined by an integrated weight scale with impedance. Physiological parameters from a separate monitoring system, for example a glucose monitoring system. Activity level, as a proxy for sweating and body urine processing, can be derived from data generated by a patient’s wearable device (e.g., smart watch) and/or smart phone. Stress level approximations can be derived from the patient’s use of his/her smart phone (e.g., leveraging apps that monitor how/when the phone is used). Stress level approximations can alternatively be determined by an analysis of the patient’s sympathetic state that is otherwise derived from ECG cardiac waveform analysis, with the cardiac waveform data being provided by a separate device with heart monitoring capabilities. Heart rate monitoring data can be provided by a separate device, as can sleep monitoring data. Posture and/or patient fatigue can be estimated by an external device with camera or video capabilities. These and other peripheral data inputs can be integrated into the parameter-based algorithms of the present disclosure, utilized to adjust sensing and/or stimulation.
[0110] Returning to FIG. 3, in other embodiments of the present disclosure, Sill leak causing events experienced by the patient are characterized and cataloged overtime (e.g., saved in a memory of the control portion 70, saved in a memory of an external device in electronic communication with the control portion 70, etc.). The characterization of each leak causing event can take various forms, and in some examples entails data or information generated by one or more sensors associated with the patient at the time of (or immediately preceding the time of) the leak causing event. The pattern or waveform of sensor signal(s) (including directionality or other vector-type data) at or immediately preceding a particular leak causing event can serve as a “characteristic fingerprint” of that particular leak causing event, with some systems, methods and algorithms of the present disclosure reviewing the patterns or waveforms as a form of signal process mining. Once a correlation between sensor signal(s) pattern, waveform or other sensor signal parameter(s) and a leak causing event has been established with an acceptable degree of confidence, methods and algorithms of the present disclosure can better determine a probability of a future leak event occurrence and can prompt stimulation accordingly (e.g., greater amplitude of simulation could be applied for some types of events, stimulation could be withheld for other types of events less likely to produce a leak, etc.). For example, where the control portion 70 has been programmed and/or has learned over time that a particular characteristic fingerprint of sensor signal patterns, waveforms or other parameters indicates, with a relatively high degree of confidence, onset of a leak causing event, when that same (or similar) fingerprint is subsequently identified in the sensor signal(s) currently being generated for the patient, the methods and algorithms of the present disclosure can be formatted to take a designated action. In further embodiments, a characteristic fingerprint of sensor information of a false positive event (e.g., an event or activity likely causing a sensor signal to meet a designated triggering criteria but that is otherwise unlikely to result in a leak, such as riding in a vehicle) are also available to the control portion 70. For example, where the control portion 70 has been programmed and/or has learned over time that a particular characteristic fingerprint of sensor signal patterns, waveforms or other parameters indicates, with a relatively high degree of confidence, the onset of a false positive event, when that same (or similar) fingerprint is subsequently identified in the sensor signal(s) currently being generated for the patient, the methods and algorithms of the present disclosure can be formatted to take a designated action. With these and related embodiments, some systems and methods of the present disclosure can better determine a probability of a leak event and, for example, greater amplitude of stimulation could be applied for some types of events or stimulation could be withheld for other types of events less likely to produce a leak. [0111] In some examples, the control portion 70 can be programmed to deem that a currently-generated sensor signal waveform is sufficiently similar to a saved characteristic fingerprint of a leak causing event (and thus prompt or trigger stimulation delivery) utilizing a probability assessment of a similarity of the signal to the fingerprint by comparing multiple characteristics and computing a composite score. For example, the probability of signal characteristics being similar to the characteristic fingerprint can be based upon amplitude, number of signal (e.g., pressure) spikes, interval between spikes, etc. A composite probability can also be established to determine if the currently-generated sensor signal waveform is sufficiently similar to the characteristic fingerprint to trigger the delivery of stimulation. [0112] In some embodiments a catalog or library of different types of leak causing events (and optionally false positive events) specific to the patient can be maintained by, or can be electronically accessed by, the control portion 70, along with the characteristic fingerprint of the sensor signal(s) corresponding with each type of leak causing event. FIG. 19 presents one non-limiting summary of a characteristic fingerprint catalog 800 that may be stored by, or available to, the control portion 70 (FIG. 3). In addition to the characteristic fingerprint, the catalog or library can include one or more designated actions to be implemented when current sensor activity in the patient is identified as implicating a particular characteristic fingerprint. Alternatively or in addition, the control portion 70 can be programmed with one or more prediction-type algorithms that predict the onset of a leak causing event based upon a comparison current sensor readings and/or parameters with the cataloged fingerprints; based on this comparison (and optionally other factors), where onset of a leak causing event is predicted with an acceptable level of confidence, the control portion 70 can prompt the delivery of stimulation to the patient and/or take other actions. In some non-limiting examples, the control portion 70 can be programed to trigger a brief, low level (e.g., lower amplitude) stimulation pattern as a notification to the patient that a leak event pattern has been recognized and onset of a leak has been predicted. With these and related embodiments, depending upon the predicted likelihood of a leak event, the algorithm can further be programmed to deliver therapeutic stimulation for leak prevention following delivery of this “warning” to the patient. In other examples, the prediction algorithm operates to log data without stimulation delivery to assess patient behaviors and system function. In yet other non-limiting algorithms, a particular characteristic fingerprint can implicate a patient’s attempt or desire to void; under these circumstances, when currently-observed sensor data is found to correlate with the “attempt to void” fingerprint, the control portion 70 can be programmed to suppress stimulation delivery. Regardless, the control portion 70 can be programmed to continuously, periodically, or triggered (e.g., by sensed events) review sensor readings with the fingerprints of the catalog, and implement a designated action when a match or correlation is found or identified. With these and related embodiments, a tolerance on the characteristic fingerprint detection can be assigned; alternatively, a tolerance can be set/calibrated during a set up and calibration period. As a point of reference, “tolerance” is the range of a parameter or group of parameters that is set or established such that when the measure values fall within this range, stimulation is delivered and/or the event is marked and logged. In other examples, fingerprint-related algorithms of the present disclosure are formatted to determine or implicate the likelihood that a concurrently observed condition is likely to result in a leakage event; if a leak is likely, stimulation is applied. The likelihood may or may not be explicitly calculated.
[0113] By way of non-limiting example, FIG. 20 presents a simplified representation of methods and algorithms of the present disclosure utilized with a treatment system arrangement that includes one or more sensors formatted and positioned on the patient to signal information indicative of pressure (e.g., abdominal pressure) and one or more sensors formatted and positioned on the patient to signal information indicative of movement (e.g., an accelerometer). With this in mind, FIG. 20 plots a signal 850 from the pressure-related sensor over time, and a signal 852 from the motion-related sensor over the same period of time. A pressure-related criteria 860 (e.g., threshold value) is assigned or generated with respect to the pressure-related sensor signal 850, and a motion-related criteria 862 (e.g., threshold value) is assigned or generated with respect to the motion-related sensor signal 852 (e.g., via various programming and/or algorithms). Moreover, with the arrangement of FIG. 20, the control portion 70 (FIG. 3) maintains or has access to a characteristic fingerprint catalog or library; within this library, a characteristic fingerprint is provided for a sneeze and an electronic fingerprint is provided for a riding in vehicle false positive. The sneeze fingerprint includes the motion sensor signal exhibiting three pulses in rapid sequence, immediately followed by a rapid rise in the pressure sensor signal. The riding in a vehicle false positive includes a rapid rise followed by a steady state in the motion sensor signal while the pressure sensor signal remains at least 50% below the pressure-related criteria 860.
[0114] At time T1 , the pressure-related sensor signal 850 meets the pressure-related criteria 860, and a combination of the pressure-related sensor signal 850 and the motion-related sensor signal 852 does not implicate any of the fingerprints of the catalog; in response, the algorithm prompts or triggers the delivery of stimulation to the patient (e.g., at a predetermined level for a predetermined length of time) in accord with standard triggering algorithm/programming. At time T2, the motion- related sensor signal 852 has three rapid pulses followed by a rise in the pressure- related sensor signal 850. These sensor signal parameters are recognized as matching or implicating the sneeze fingerprint. In response, the algorithm(s) predicts the onset of a leak causing event, and prompts or triggers (or operates instructions from the catalog) the delivery of stimulation to the patient at an elevated level. Notably, at time T2, the pressure-related sensor signal 850 meets the pressure- related criteria 860. However, the stimulation actually delivered to the patient is at a higher level (e.g., higher intensity) than the standard trigger programming due to the characteristic fingerprint recognition. At time T3, the motion-related sensor signal 852 meets the motion-related criteria 862. However, at this same time, the pressure- related sensor signal 850 is at least 50% below the pressure-related criteria 860 and the motion-related sensor signal 852 experienced a rapid rise followed by steady state. These sensor signal parameters are recognized as matching or implicating the riding in a vehicle false positive sneeze characteristic fingerprint. In response, stimulation is not delivered to the patient.
[0115] Returning to FIG. 3, the patient-specific, characteristic fingerprints of the present disclosure can be generated in various fashions. In some embodiments, the fingerprint of events such as a cough, sneeze, getting up from a chair, squatting, etc., can be learned by the control portion 70 (or by a device in electronic communication with the control portion 70) relative to the specific sensor-related signature of these events, for example accelerometer signal(s), EMG signal(s), bioimpedance signal(s), strain or other signals. As an example, a cough may produce a specific, repetitive pattern of accelerations, combined with a specific abdominal EMG signal that is otherwise distinct from other events such as a sit up. Distinguishing between a cough that is likely to cause an Sill leak in an individual from a sit up, which may be unlikely to cause an SUI leak, can allow the treatment system to avoid false positive detections and unwanted stimulation deliveries. The fingerprint can include a plethora of different characteristics including, but not limited to: frequency components of sensor signals within an event; the shape of the sensor signal in frequency or temporal domains over time (e.g., a pattern where a smaller pre-pulse occurs, followed by a rapid increase in pressure (or pressure-related signal)); order or combination of detected events; frequency of events; a combination of different characteristics. Regardless, the patient can inform the control portion (e.g., via the external device 68) when s/he is experiencing or has just experienced an event of interest (e.g., cough, sneeze, etc.), and the control portion 70 can then review and/or record the fingerprint associated with that event.
[0116] By way of non-limiting example, FIG. 21 provides possible signal characteristics as a fingerprint of a sneeze, with three consecutive sneezes being recorded. The example of FIG. 21 presents sensed vaginal pressure 870 and root mean square (RMS) of magnitude acceleration (ACC) 872, with three consecutive sneezes identified at 874. As highlighted, fingerprint characteristics generated by a pressure sensor can include a pre-sneeze pulse waveform 876, a characteristic interval 878 between a pre-sneeze pulse peak and pressure rise before sneeze, and a pressure waveform slope 880 at sneeze onset. FIG. 22 provides possible signal characteristics as fingerprint of a cough, with three consecutive coughs being recorded. The example of FIG. 22 presents sensed rectal pressure 884, with three consecutive coughs identified at 886. As highlighted, fingerprint characteristics generated by a pressure sensor can include a pre-cough pulse waveform 888, a characteristic interval 890 between a pre-cough pulse peak and pressure rise before cough, a pressure waveform slope at cough onset 892 and a pressure waveform slope increase at cough 894.
[0117] Returning to FIG. 3, in other embodiments, the sensor-based fingerprints of Sill leak causing events or related events can be identified in a manual learning or self-learning period, for example performed before the therapy device is activated or optimized. Various techniques or devices could be utilized as part of or during the learning period, such as external diagnostic signals such as from urodynamics testing in a laboratory, clinical setting, and/or home setting. For example, an external leak detection device (e.g., pad-type wetness sensor) could be used to feedback information to the control portion 70 (or other electronic device in communication with the control portion 70) on the fingerprint of the types and magnitude of events that cause an Sill leak and the fingerprint of events that will likely not cause such a leak and stimulation should be suppressed. In other examples, the external diagnostic signal can include bladder volume as determined by ultrasonography or similar techniques as the signature of an Sill leak causing event could be specific to bladder volume.
[0118] An example method 900 of the present disclosure for learning fingerprints is shown in FIG. 23. In some embodiments, the electronic fingerprint learning methods of the present disclosure, such as the method 900, can be facilitated by at least one electronic device in electronic communication with the control portion 70 (FIG. 3) (or with another computer-like device programmed to generate/store electronic fingerprints). The electronic device can be a remote-type device, programmed (e.g., operating software or hardware) to facilitate characteristic fingerprint learning. Alternatively, the electronic device can be a consumer electronic device (e.g., a smart phone) operating a software application programed to facilitate characteristic fingerprint learning. The electronic device can be operated by one or more of the patient, a clinician, and a third party. At step 910, one or more external diagnostic devices are optionally connected to the patient to sense patient parameters of interest (e.g., catheter-based external diagnostic devices for sensing pressure and/or bladder volume). In other embodiments, the method 900 can be performed following placement of a treatment system to the patient, including all sensors provided with the treatment system. Regardless, all sensor data is continuously provided or available to the control portion 70 (or other electronic device operating to generate a fingerprint catalog or library). At 912, the patient is prompted to perform an action or event of interest. For example, a clinician overseeing the testing can prompt the patient to perform the desired action (e.g., selected by the clinician and noted on the electronic device, selected by the electronic device and displayed to the clinician, etc.). In other embodiments, the electronic device is operated by the patient and displays a particular action or event to be performed. In the non-limiting example of FIG. 23, the action is a cough. The so-performed action will or will not cause a leak at 914. If a leak did not occur (“N” at step 914), the patient is prompted to perform the same action or event, but with greater intensity or vigor at 916 (e.g., as instructed by the clinician, as instructed by a display of the electronic device, etc.). Optionally, the “no leak” result can be recorded by the patient or clinician at the electronic device. [0119] This process continues until the performed action or event causes a leak (“Y” at 914), which is noticed and marked by the patient at 918 (e.g., the patient and/or the clinician operates the electronic device to note that a leak occurred, an external diagnostic device senses a leak, etc.). Upon being informed that a leak occurred, the control portion 70 operates, at step 920, to record a fingerprint of available sensor data (e.g., external diagnostic device sensors (where provided), on-board sensors provided with an installed treatment system, etc.) during a timeframe of the noted leak event (e.g., a time period beginning 10 seconds before a time of the leak event and ending 10 seconds after the time of the leak event). A trigger point (e.g., time when stimulation would be beneficial) or time the leak event occurred is also recorded within the sensor fingerprint. The patient is then, at step 922, prompted to perform another action or event of interest as described above, and the process continues until fingerprints of all actions or events of interest have been created.
[0120] The method 900 is one example of characteristic fingerprint learning techniques of the present disclosure. Other approaches can be employed. Returning to FIG. 3, in some embodiments, one or more steps can be utilized for learning the fingerprint of a particular type of leak causing event or action, and various programming or operational steps can be implemented for predicting leak causing events and/or acting upon circumstances in which current sensor signals implicate a fingerprint of a particular type of leak causing event or action. For example, learning a characteristic fingerprint for laughing can entail inducing or prompting the patient to laugh at different intensities and optionally in different body positions (e.g., standing, sitting, etc.). During this time, the laughing event can be marked manually by the clinician or patient, and the results of whether or not a leak occurred can be communicated to the control portion 70 via an external electronic device. Laughing for a particular individual will have unique sensor signal characteristics (fingerprint) such as a given frequency of acceleration, bioimpedance oscillations, abdominal EMG pulsing waveform, direct intraabdominal pressure oscillations, etc. When the fingerprint of a laugh is marked for a patient as causing a leak, the control portion 70 can be programmed or configured to deliver stimulation during this event at the earliest indications of event onset to prevent or minimize a leak. Conversely, for laugh events deemed unlikely to cause a leak, stimulation delivery can be suppressed during this time to prevent unwanted stimulation. The laugh for a particular patient may be found to build in a characteristic way, with increasing successive pulses (e.g., accelerations or EMG voltage peaks); the corresponding fingerprint can thus be programmed to recognize a currently-occurring laugh capable of causing an Sill leak early in the cycle and deliver stimulation energy based on the prediction that this type of currently-occurring laugh appears consistent with the laugh fingerprint that is otherwise known to eventually cause a urine leak.
[0121] Learning a characteristic fingerprint for sneezing can be based upon the recognition that an abdominal wall muscle contraction precedes the rapid increase in intraabdominal pressure of a sneeze to brace the body and increase the velocity of air ejected. Thus, in some embodiments, the sensing fingerprint of a sneeze for an individual that causes an SUI leak can be recorded and identified during the learning period such that when the early phase of the sneeze fingerprint is identified, simulation energy can be delivered. Furthermore, the early phase of the sneeze fingerprint can be distinct from the fingerprint of an exercise activity that also causes a similar abdominal wall contraction but that does not cause a leak (or otherwise implicate a need for stimulation). The difference in this case might be the accelerations detected as part of the exercising activity that do not exist in the early phase of the sneeze, allowing the control portion 70 to differentiate between the two events and only delivery stimulation during the sneeze.
[0122] Learning a characteristic fingerprint for coughing can be based upon the recognition that the fingerprint of a cough for an individual can be distinct, for example based on the slower pulsing frequency as compared to a laugh. Other sensed differences between a cough and other events can include number of pulsing cycles (EMG, IAP, accelerations, etc.) and the way each successive pulse builds into a crescendo. As previously described, if during the learning mode a cough of a given fingerprint is likely to cause an Sill leak, the control portion 70 can be programmed or configured to deliver stimulation energy in the early phase of the fingerprint of the coughing event.
[0123] Learning a characteristic fingerprint for exercising can be based upon a recognition that with some exercising events, urine leakage may occur (e.g., jumping) versus others that do not cause a leak in a given individual (e.g., performing a sit up). The sensing fingerprint and associated algorithms would be different for each exercise type. For example, with jumping and performing a sit up, an abdominal EMG signal likely increases but the accelerations associated with the jump would be distinct from the slow moving sit up, making the fingerprint for each unique and allowing the control portion 70 to deliver stimulation when the early phase of a jump is detected versus a sit up where stimulation should not be delivered if no leakage event is likely to occur.
[0124] The fingerprints of riding in a car, train, plane or other vehicle will involve accelerations and, to some extent, EMG, bioimpedance and other signal changes. The patient can communicate to the control portion 70 during the learning phase regarding the type of environment and whether a leak occurred such that the control portion 70 can detect the fingerprint of the motion of the transportation and other active signals to in the future determine if a leak is likely based on historical information.
[0125] In some embodiments, the characteristic fingerprints of individual events as described above are tracked over time during therapeutic use of the system 50, and are utilized as feedback for automated adjustment of triggering or prediction algorithms being operated by the control portion 70. In this way, the stimulation deliveries completed based on prediction of Sill causing events can be checked against the fingerprint of the complete Sill causing event and corrections can then be automatically made to the prediction algorithms to improve predictions of the SUI leak causing events. By way of example, the control portion 70 could be programed to operate a sensor-based prediction or triggering algorithm for a cough event that entails detecting a pre-cough pressure pulse of a given width and amplitude followed by detecting a rate of change pressure increase over a minimum amount, all within a given time window; if all of these criteria for prediction of a cough are not met, the sensor-controlled algorithm would not prompt delivery of stimulation. However, the complete fingerprint of the cough could later be recognized by the control portion 70 after three cough pulses as reflected by FIG. 22. For example, the patient could inform the control portion 70 that a cough-caused leak event had just occurred (e.g., via the external device 68, tap gesture, etc.); in response, the control portion 70 is programmed to obtain/review a characteristic fingerprint of sensor signals at the time of the patient-marked leak event. In other examples, the patient could inform the control portion 70 when they believe stimulation will become increasingly necessary; in response, the control portion 70 is programmed to correlate a corresponding fingerprint characteristics before or during the so-indicated point in time so that in the future, stimulation is automatically triggered when similar fingerprint characteristics are recognized. In other examples, the control portion 70 can be programmed to identify and log fingerprints that are similar to fingerprints or levels that have already been identified as events to trigger stimulation. With these and related embodiments, a clinician can review the log file and surmise that this new fingerprint(s) correspond to a leak event that could be prevented or mitigated in the future by stimulation treatment, for example under circumstances where the patient was unable to accurately mark a particular leak event. Regardless, a time frame (e.g., start time and/or length) of a noted fingerprint can be designated in various fashions. In some embodiments, a clinician will manually identify the sensor signal(s) and time frame for a particular fingerprint corresponding to a leak marker made by the patient. In other embodiments, the control portion 70 can be programed to automatically identify the most recent event that meets specific criteria and log that fingerprint event as the most likely causal event for a patient-marked leak. The criteria can include, for example, a peak pressure, rate of change in pressure, similarity to an already entered fingerprint designated by an algorithm to prompt delivery of stimulation, etc. In other embodiments, an iterative process can be employed whereby the control portion 70 automatically identifies the most likely causal event and adds it to the fingerprint catalog or other criteria for triggering stimulation. Alternatively or in addition, iterative rules can be employed for adding new fingerprints. For example, a rule such as a new fingerprint is only designated if it precedes a marked leak event a minimum of X number of times. Additionally, a clinician or third party may also manually designate new fingerprints used to trigger stimulation after review of logged files, after a recalibration session, etc.
[0126] Continuing the above example and assuming the so-obtained fingerprint is that of FIG. 22 and further that the predictive or triggering algorithm failed to correctly identify the leak causing event, the control portion 70 can be programmed to automatically correct or change the cough-type prediction or triggering algorithm to increase the accuracy of the prediction algorithm. With respect to the example of FIG. 22, these changes could include a wider window of time to detect the rapid pressure increase of the onset of the cough, a wider range of slopes of the cough onset pressure rise or other changes. The control portion 70 can be programmed to perform a wide variety of other algorithm adjustments and/or a clinician can effect an algorithm adjustment. In some embodiments, the control portion 70 can be programmed to review multiple patient-marked leak events over time and identify the most common type of sensor signal pattern(s) preceding a particular patient-marked leak event that otherwise has characteristics similar to known leak-related fingerprints to determine what adjustments are needed. This can be an iterative process that may require days to improve performance but serves as a viable method for continuous adaptation and therapy improvement. Machine learning can be employed by the control portion 70 to detect relevant signal patterns.
[0127] In some examples, algorithm adjustments can be implemented by the control portion 70 without direct knowledge of a cause of a particular patient-marked leak event. In other embodiments, the patient can provide information (e.g., via the external device 68) explaining a believed cause of a particular patient-marked leak event (e.g., sneeze, jump, etc.). The type of causal event information can be reviewed by a clinician (or automatically by the control portion 70) to help trouble shoot and identify changes to an algorithm or even lifestyle/activity changes recommended for the patient that might be beneficial. Moreover, as time goes on, aggregate data from multiple treatment systems can be used to help refine the algorithms, including activity classification.
[0128] In related embodiments, characteristic fingerprints of the present disclosure can be utilized for correcting or adjusting a predictive or triggering algorithm being operated by the control portion 70 that is otherwise resulting in false positive detections of Sill leak causing events (and thus prompting stimulation delivery when not otherwise needed by the patient). The control portion 70 can be programmed to make automated changes that decrease sensitivity of the algorithm that in turn reduce the number of times stimulation energy is delivered when not needed, with an extent of the changes being dictated by a review of an electronic fingerprint of sensor signals at the time of the false positive event. For example, the patient could inform the control portion 70 that a false positive event (e.g., stimulation delivered when not needed or desired by the patient) has just occurred (e.g., via the external device 68, tap gesture, etc.). In response, the control portion 70 is programmed to obtain/review a characteristic fingerprint of sensor signals at the time of the patient- marked, false positive event, as well as the particular algorithm that triggered the stimulation. The so-obtained fingerprint is then analyzed by programming of the control portion 70; for example, identifying the sensor signal parameters of the fingerprint that caused the corresponding algorithm to trigger stimulation. In some embodiments, the correction(s) to the predictive or triggering algorithm would be implemented so that subsequent, similar sensor signal parameters would be less likely to cause unwanted stimulation delivery. In other examples, a grading of certainty can be designated or assigned to the predictive or triggering algorithm in question based upon the fingerprint. For an algorithm found over time to have a history of less certainty (combination of positive/false positive), a patient-selectable sensitivity level could be implemented or applied to the algorithm. For example, if the patient sets the sensitivity level to “high”, the algorithm will operate to more aggressively deliver stimulation (in effect, the patient is accepting more false positives in exchange for fewer missed leak events). [0129] FIG. 24 illustrates, in simplified form, another treatment system 1000 of the present disclosure. The treatment system 1000 includes the implantable pulse generator 64, stimulation element(s) 66, sensor(s) 62, and a patient remote 68 as described above, along with one or more external marker devices 1010. The marker device 1010 can assume a wide variety of forms, and is generally configured to be wirelessly detected by one or both of the implantable pulse generator 64 and the patient remote 68 when in relatively close proximity thereto. For example, the marker device 1010 can include RFID tag-type circuitry, capable of being detected by corresponding RFID reader components provided with the pulse generator 64 and/or the patient remote 68 (on a passive or active reader basis). In other embodiments, the marker device 1010 can include circuitry components configured to send out a wireless signal that can be detected by the pulse generator 64 and/or the patient remote 68 when nearby (e.g., Bluetooth signal). Regardless, the marker device 1010 can have a small footprint or size (e.g., akin to a small tag), and the control portion 70 (FIG. 3) is programmed to perform one or more operational functions when the marker device 1010 is detected as being proximate the pulse generator 64 and/or the patient remote 68. With these and related embodiments, the marker device 1010 can be placed where leak events are more likely to happen (or less likely to happen), with the system 1000 then operating in a more targeted fashion.
[0130] In some embodiments, detected presence of the marker device 1010 can effect automated adjustment of triggering or prediction algorithms being operated by the control portion 70. For example, the control portion 70 (FIG. 3) can be programmed with a triggering or prediction algorithm that prompts delivery of stimulation when a signal from the sensor 62 (or a parameter of the signal) meets a designated criteria (e.g., exceeds a threshold value); with these and related embodiments, the criteria applied by the triggering or prediction algorithm is altered in a manner that increases the likelihood of being met (e.g., a threshold value criteria is decreased, etc.) when presence of the marker device 1010 is detected (thus increasing sensitivity of triggering or prediction algorithm). In other embodiments, the criteria can be altered in a manner that decreases the likelihood of being met (e.g., a threshold value criteria can be increased) when presence of the marker device 1010 is detected (thus decreasing sensitivity of the triggering or prediction algorithm). In other embodiments, detected presence of the marker device 1010 can effect automated adjustments to stimulation settings. For example, the control portion 70 can be programmed to prompt delivery of stimulation at a predetermined level when a signal from the sensor 62 (or a parameter of the signal) meets a designated criteria; with these and related embodiments, the level of the stimulation delivered to the patient can be increased when presence of the marker device 1010 is detected.
[0131] The marker device 1010 (or a plurality of marker devices 1010) can be placed in an environment where leakage events are more likely to occur and/or the patient desires a higher level of security or protection against leaks. In other embodiments, the marker device 1010 can be attached to specific articles of clothing associated with specific activities, such as exercise clothing, running shoes, etc.
[0132] FIG. 25 illustrates the treatment system 50 described above implanted to a patient. In some embodiments of the present disclosure, an external device 1100 with internet access can be employed to facilitate assessment of, and/or adjustments to, algorithms being operated by the implanted treatment system 50 (e.g., triggering or predictive algorithms). In general terms, the external device 1100 includes a controller and a memory. The controller comprises at least one processor and associated memories, with the processor executing various programming as described below. The external device 1100 further includes appropriate circuitry components for communicating with the implanted pulse generator 64, the patient remote 68, and the cloud 1200. The external device 1100 operates to secure log files from the implanted device 64 to be available for analysis by a clinician. The downloaded files can be automatically uploaded to the cloud 1200 or can be downloaded via a manual prompt by the patient at the external device 1100. Furthermore, the external device 1100 can include sensing capabilities that may allow analysis of the treatment system algorithm(s) and adjustments or recommendations for adjustments as described below. Regardless, the external device 1100 can be located in an area convenient to the patient, for example when performing various testing procedures relating to continence treatment (e.g., a bathroom or bedroom of the patient’s home). The external device 1100 (and/or the patient remote 68) can be utilized by the patient as part of an assessment session, or to enter performance information periodically. This performance information can, in some embodiments, be assessed by the external device 1100 over time and a recommendation can be made to the patient for when a recalibration session or adjustments are needed.
[0133] One example of a method 1300 for assessing and/or adjusting triggering or predictive algorithms being operated by the treatment system 50 is shown in FIG. 26. Prior to implantation of the treatment system 50, a clinician can perform various screening tests at 1310 to evaluate the usefulness of the system 50 for the patient. As part of the pre-implant screening, the clinician can evaluate potential settings for the system 50, for example triggering or leak prediction criteria (e.g., thresholds), stimulation settings, etc. At 1312, during and/or immediately following implantation, a clinician can perform various intraoperative tests, for example to arrive at initial algorithm settings. The algorithm settings (as well as overall operation of the system 50) can be established, evaluated, and adjusted by a clinician during in-clinic or inlab sessions with the patient at 1314. In addition, the algorithm settings can be automatically established, evaluated, and adjusted by the control portion 70 (FIG. 3) based upon various in-home procedures performed by the patient at 1316. Regardless of how the algorithm settings are established, an assessment of one or more algorithms being operated by the system 50 is performed at step 1318. In particular, and with cross-reference to FIG. 25, with the patient positioned in close proximity to the external device 1100 (e.g., sufficiently close for the external device 1100 to wirelessly communicate with one or both of the pulse generator 64 and/or the patient remote 68), the patient is prompted (e.g., via a display of the external device 1100, the patient remote 68, a smart phone operating a testing software application, etc.) to perform one or more test procedures formatted to evaluate algorithm performance, several examples of which are described below. The testing results and/or other logged information of interest stored in a memory of the pulse generator 64 and/or the patient remote 68 are stored on the external device 1100. The stored information can be uploaded from the external device 1100 for review by a clinician (e.g., via the cloud 1200) and/or can be reviewed by programming of the external device 1100. This review facilitates a determination at step 1320 as to whether or not the algorithm(s) being assessed is performing adequately. Where the algorithm is deemed to be performing adequately (“Y” at 1320), additional algorithm performance assessment can be performed at 1318. Where the algorithm is deemed to not be performing adequately (“N” at 1320), algorithm adjustment(s) can be effected either by an in-clinic or in-lab session (step 1314) or an in-home session (step 1316).
[0134] In some embodiments, the external device 1100 is formatted to assist in assessing triggering or predictive algorithm(s) being operated by the system 50. For example, while wirelessly communicating with the pulse generator 64 (and/or the patient remote 68), the external device 1100 is configured to alert the patient when the system 50 has been triggered to deliver stimulation (e.g., emitting a light, tone, vibration, etc.). The patient is then prompted to record whether the so-triggered stimulation was desired or otherwise appropriate, for example via a keypad, touch screen, dedicated button, or similar feature provided with the external device 1100. Alternatively or in addition, the patient can record at the external device 1100 instances or events in which a leak occurred but no stimulation was delivered. The external device 1100, or a clinician reviewing information stored by the external device 1100, can adjust one or more triggering or predictive algorithms being operated by the treatment system 50. For example, the control portion 70 (FIG. 3) can be programmed with a triggering or prediction algorithm that prompts delivery of stimulation when a signal from a sensor (or a parameter of the signal) meets a designated criteria (e.g., exceeds a threshold value). Under circumstances where the so-programmed algorithm prompts delivery of stimulation, the patient is notified of the triggered stimulation by the external device 1100, and the patient then records at the external device 1100 that the so-delivered stimulation was not desired or needed, the external device 1100 (and/or a clinician reviewing information stored by the external device 1100) adjusts or lowers a sensitivity of the algorithm triggering criteria (e.g., increases the threshold value) to avoid similar, false trigger-type occurrences in the future. Alternatively, under circumstances where the patient records a leak event at the external device 1100 when stimulation was not otherwise delivered, the external device 1100 (and/or a clinician reviewing information stored by the external device 1100) adjusts or increases a sensitivity of the algorithm triggering criteria (e.g., decreases the threshold value) to better assist in avoiding a similar leak event in the future and/or increases an intensity of stimulation delivered in response to the algorithm.
[0135] In some embodiments, one or more additional components can be utilized with the external device 1100 to access performance of triggering or predictive algorithms being operated by the system 50. For example, as shown in FIG. 27, an intraabdominal pressure measuring device (e.g., a disposable rectal or vaginal manometry catheter device) 1400 can be installed to the patient. Sensed information from the pressure measuring device 1400 is delivered to the external device 1100 (wired or wireless communication). During an assessment session in which the external device 1100 to operates notify the patient when stimulation delivery has been triggered and record the patient’s observations as described above, the measured intraabdominal pressure readings are provided to the external device 1100 to better assist in determining if the triggering or predictive algorithm being assessed and related implantable sensors are working properly. For example, the control portion 70 (FIG. 3) can be programmed with a triggering or prediction algorithm that prompts delivery of stimulation when a signal from an implanted sensor (or a parameter of the signal) otherwise implicating abdominal pressure meets designated triggering criteria (e.g., exceeds a threshold value). Under circumstances where the so-programmed algorithm prompts delivery of stimulation, the patient is notified of the triggered stimulation by the external device 1100, the patient records at the external device 1100 that the so-delivered stimulation was not desired or needed, and a comparison of the implanted sensor signal with the pressure measuring device 1400 signal reveals that the implanted sensor indicated a distinct pressure change that was not otherwise repeated by the pressure measuring device 1400, the external device 1100 (and/or a clinician reviewing information stored by the external device 1100) could determine that the implanted sensor requires adjustment. In yet other embodiments, the control portion 70 can be programmed to adjust the triggering or predictive algorithm in response to the so- identified implanted sensor performance concerns (or a clinician can implement the adjustments). For example, if the sensitivity of an implanted pressure sensor appears to be drifting down over time (e.g., through repeated calibration sessions) as compared to the pressure measuring device 1400 (e.g., manometry catheter), the algorithm can be progressively adjusted to be more sensitive, thus compensating for the progressive loss of sensitivity by the implanted sensor.
[0136] Returning to FIG. 25, in other embodiments, the external device 1100 can be configured to generate or otherwise provide information indicative of voiding events and voiding or bladder volumes of the patient. For example, an external ultrasound device or probe can be provided with, or connected to (wired or wireless connection), the external device 1100. As part of an algorithm assessment session, the ultrasound device can be arranged to sense a parameter or state of the patient’s bladder. In other embodiments, bladder volume can be measured or estimated based on information from other devices/sensors, for example an implanted bladder wall strain sensor or a detector of ENG or EMG activity indicative of detrusor muscle activity. Regardless, the sensed bladder volume information or related data (e.g., a bladder volume state of full, partial, or empty) is provided to the external device 1100 and utilized in assessing and/or improving performance of triggering or predictive algorithms. For example, during an assessment session, the patient may indicate a desire to trigger stimulation and/or increase a level of delivered stimulation; under these circumstances, the external device 1100 can operate to determine a correlation between the patient’s request and corresponding bladder volume (e.g., a patient stimulation request could be found to be dependent upon bladder volume state). This correlation or preference can be utilized by triggering or predictive algorithms being operated by the control portion 70 (FIG. 3), implemented, for example, by referencing an implanted sensor otherwise formatted and located to sense information indicative of the patient’s current bladder volume. One or more triggering or predictive algorithms being operated by the control portion 70 that otherwise function, at least in part, based on a sensed or predicted bladder volume of the patient can be assessed. For example, a triggering or predictive algorithm can be formatted to have a varying level of sensitivity based on sensed or designated bladder volume (e.g., as the patient bladder volume increases, the triggering or predictive algorithm is more likely to deliver stimulation). By knowing the patient’s actual bladder volume, the performance of this algorithm can be better assessed by the external device 1100 (or clinician reviewing information stored by the external device 1100). In other examples, where the implanted system 50 does not otherwise include sufficient sensor capacity to reliably estimate current bladder volume, the patient can select trigger and/or stimulation levels corresponding to the bladder volume of choice to either minimize the probability of a leak or reach a balance between comfort and leak protection. In yet other embodiments, the external bladder sensing functionality can be used to confirm (or adjust) that the sensitivity and other settings utilized by one or more triggering or predictive algorithms are established relative to the patient being in a stressed state (e.g., full bladder), and thus when most likely to experience a leak event.
[0137] In yet other embodiments, and as shown in FIG. 28, a urethral catheter device 1450 (e.g., including a disposable or cleanable/re-sterilizable/reusable catheter) can be installed to the patient. Information from the urethral catheter device 1450 is provided to the external device 1100 (via direct or wireless connection). During an assessment session, the patient is prompted to perform one or more causal activities (e.g., Valsalva, coughing, etc.), with information generated by the urethral catheter device 1450 utilized to determine if adequate stimulation is being delivered to prevent leaks and effect an acceptable increase in urethral pressure. For example, if stimulation is triggered at an apparently correct time during a prompted Valsalva maneuver (when a leak is just started but not substantially mitigated) but the urethral pressure is determined to not have increased to an acceptable level, the external device 1100 (or a clinician reviewing information from the external device 1100) can automatically change stimulation algorithms being operated by the control portion 70 (FIG. 3) to increase stimulation amplitude. Alternatively, the patient may elect to increase stimulation amplitude within the range that the clinician has established, with the external device 1100 recording this patient-prompted change. In some examples, the urethral catheter can provide feedback to the patient regarding an electrically stimulated contraction as compared to that from a volition EUS contraction (Kegel). This, in turn, can inform the patient regarding how much more improvement is possible with increased stimulation amplitude and willingness to tolerate some additional discomfort as needed for effectiveness. Additionally, the timing of the triggered stimulation can be determined directly by the time at which the EUS pressure increases from stimulation relative to when the patient observes a leak. If, for example, the stimulation causes the EUS pressure to occur after the leak is observed to initiate, the trigger algorithm must be adjusted to provide stimulation earlier. Conversely, if the stimulation is shown through the EUS pressure increase to have occurred before the leak, then algorithm timing is not the issue and increased stimulation amplitude would be the recommended change during the calibration/set up session.
[0138] In yet other embodiments, and as shown in FIG. 29, one or more external sensors 1460 can be incorporated with, or in wireless communication with, the external device 1100 (via direct or wireless connection). The external sensor(s) 1 60 can assume various forms and in some embodiments is/are of a type and format corresponding with any implanted sensors (not shown) provided with the treatment system 50. For example, the sensor(s) 1460 can be or include EMG, bioimpedance, accelerometer, and other external sensors. As part of an assessment session, the patient is directed to locate the external sensor(s) 1460 at the relevant anatomy. Signals from the external sensor(s) 1460 during the assessment session can be compared with implanted sensor signals that are otherwise being used as the basis for triggering or predictive algorithms of the treatment system 50. From this comparison, the external device 1100 (and/or a clinician reviewing information stored by the external device 1100) can troubleshoot functioning of the implanted system 50. Moreover, the external sensor signals can provide diagnostic information to the patient and clinician. For example, the external sensor signal information (as well as other data gathered by the external device 1100) can be provided to a clinician through the cloud 1200, memory storage media (SD card, external solid state drive, etc.) provided with the external device 1100, via the patient remote 68, etc.
[0139] Returning to FIG. 3, while some of the systems, methods, and algorithms of the present disclosure have been described primarily as treating urinary incontinence, in other embodiments the systems and methods of the present disclosure can include providing a therapeutic benefit through training or exercising of relevant muscles. For example, a training protocol or therapeutic mode can be programmed/implemented by the control portion 70 whereby stimulation is delivered on a schedule (e.g., 8 seconds or longer bursts) for creating muscle contraction of the EUS and/or other continence muscles as a form of training, thereby providing a therapeutic effect of increasing continence muscle effectiveness and/or improve fatigue resistance over time. In some embodiments, the therapeutic mode does not utilize or rely upon sensor signal information; instead, once the therapeutic mode is initiated by the patient (e.g., via the external device (or patient remote) 68, tap gestures, etc.), scheduled stimulation is delivered, system is configured and programmed to operate in a treatment mode, a therapeutic mode, or both.
[0140] In yet other embodiments, the therapeutic or exercise mode can entail periods of stimulation paired with a volitional squeeze of the EUS. These and similar techniques can lead to central nervous training to improve urethral function. For example, the control portion 70 can be programmed to perform a therapeutic mode in which the pulse generator 64 delivers stimulation while at the same time the patient exerts effort to squeeze the urethral muscles. The stimulation coincident with patient effort sequence can be initiated in various fashions. In some examples, a stimulation/effort sequence is initiated by the control portion 70 operating to prompt the patient to squeeze (e.g., via the external device 68); in other examples, the stimulation/effort sequence is initiated by the patient (e.g., the patient enters a prompt at the external device 68 that is interpreted by the control portion 70 to begin a stimulation/effort sequence). Regardless, FIG. 30 is a stylistic illustration of two stimulation/effort sequences. Line A is a representation of a digital signal interpreted to initiate a stimulation/effort sequence. As described above, the initiation signal A can be generated by a prompt from the system 50 (FIG. 3) to the patient indicating when to squeeze the urethra, or an indication provided by the patient to the control portion 70 that s/he is ready to squeeze. Line B is a representation of stimulation being delivered to the patient. Line C is a representation of the patient voluntarily squeezing his/her urethral muscles (where increased amplitude indicates increased effort).
[0141] Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein. Further, any of the systems, methods, and algorithms of the present disclosure can be utilized for the treatment of other medical conditions, for example sleep disordered breathing (SDB).