Disclosure of Invention
The invention provides a method and a device for automatically detecting epilepsy of an implanted closed-loop system based on an area algorithm, which flexibly configures integrated implanted equipment to detect epilepsy in real time with low power consumption by using a hardware algorithm, and improves integration level and detection convenience.
To solve the above technical problems, one or more embodiments of the present specification are implemented as follows:
In a first aspect, a method for automatically detecting epilepsy based on an area algorithm in an implantable closed-loop system is provided, and the method is applied to an epilepsy detection device formed by a chip, and comprises the following steps:
Configuring detection parameters through an SPI (serial peripheral interface), wherein the detection parameters at least comprise a detection window size, an interval window size and a background window size in one detection period;
Collecting bioelectric signals from a living body implanted with the detection chip in real time and storing the bioelectric signals into a memory;
respectively calculating absolute values of the differences between the magnitudes of the bioelectric signals and the direct current bias according to time sequences, and sending the absolute values to an accumulator;
Based on the value in the accumulator, respectively determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window;
Judging whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
And when the judgment result is larger than the judgment result, outputting an identification signal representing the epileptic seizure so as to trigger the treatment device to perform stimulation treatment on the organism.
In a second aspect, an apparatus for automatically detecting epilepsy in an implantable closed loop system based on an area algorithm is provided, including:
The configuration module configures detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least comprise the size of a detection window, the size of an interval window and the size of a background window in one detection period;
the acquisition and storage module acquires bioelectric signals from organisms implanted with the detection chip in real time and stores the bioelectric signals into the memory;
the calculating module is used for respectively calculating the absolute value of the difference between the amplitude value of the bioelectric signal and the DC offset according to the time sequence and sending the absolute value to the accumulator;
the determining module is used for respectively determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window based on the value in the accumulator;
The detection module is used for judging whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
and the treatment module outputs an identification signal representing the epileptic seizure to trigger the treatment device to perform stimulation treatment on the organism when the judgment result is larger than the judgment result.
In a third aspect, an apparatus for automatically detecting epilepsy based on an area algorithm in an implantable closed loop system is provided, at least comprising a hardware algorithm chip for executing the method and other functional modules, wherein the hardware algorithm chip comprises a serial peripheral interface, a memory, an accumulator and a counter,
The serial peripheral interface is used for configuring detection parameters and transmitting bioelectric signals, wherein the detection parameters at least comprise detection window size, interval window size and background window size in one detection period;
The memory is used for storing the bioelectric signals collected from the organism implanted with the detection chip in real time, and the absolute values of the differences between the magnitudes of the bioelectric signals and the direct current bias are respectively calculated according to the time sequence by the counter and then sent to the accumulator;
After the area sum of the current detection window and the area sum of the background window corresponding to the current detection window are respectively determined based on the value in the accumulator, a detection enabling end judges whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
and when the judging result is greater than the judging result, the serial peripheral interface outputs an identification signal representing epileptic seizure so as to trigger the treatment device to perform stimulation treatment on the organism.
In a fourth aspect, an electronic device is provided, including:
processor, and
A memory arranged to store computer executable instructions that, when executed, cause the processor to perform:
Configuring detection parameters through an SPI (serial peripheral interface), wherein the detection parameters at least comprise a detection window size, an interval window size and a background window size in one detection period;
Collecting bioelectric signals from a living body implanted with the detection chip in real time and storing the bioelectric signals into a memory;
respectively calculating absolute values of the differences between the magnitudes of the bioelectric signals and the direct current bias according to time sequences, and sending the absolute values to an accumulator;
Based on the value in the accumulator, respectively determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window;
Judging whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
And when the judgment result is larger than the judgment result, outputting an identification signal representing the epileptic seizure so as to trigger the treatment device to perform stimulation treatment on the organism.
According to the technical scheme provided by one or more embodiments of the present disclosure, the bioelectric signals can be collected and detected in real time by adopting a hardware algorithm, and then, whether epileptic is sent to trigger stimulation treatment through the identification signal is determined according to the comparison of the ratio of the average area of the detection window to the average area of the background window in each detection period and the preset threshold. Therefore, the integration level, the detection accuracy and the convenience are improved by low-power consumption real-time detection.
Detailed Description
In order that those skilled in the art will better understand the technical solutions in this specification, a clear and complete description of the technical solutions in one or more embodiments of this specification will be provided below with reference to the accompanying drawings in one or more embodiments of this specification, and it is apparent that the one or more embodiments described are only a part of embodiments of this specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
The embodiment of the specification provides a hardware algorithm for automatically detecting epilepsy based on an area algorithm in an implantable closed-loop system, belonging to the fields of neuroscience and medical devices. The hardware algorithm can be written in Verilog hardware language or other realizable hardware languages, can accurately detect whether the bioelectric signal is abnormal in real time, and pulls up the Flag warning signal when the bioelectric signal is abnormal, at the moment, the detection enabling signal is 1, the hardware algorithm pauses the detection, and the hardware algorithm detects when the detection enabling signal is 0. The specific detection standard may refer to the following, so that signal data of a previous period (generally set to 30 s) of the abnormal signal generation time and signal data of a subsequent period (generally set to 60 s) of the abnormal signal generation time can be transmitted through the serial peripheral interface SPI protocol, and this part of data can be stored externally for use in medical research. In the whole detection process, the detection window, the background window, the interval window and the threshold value of the algorithm are adjustable for each round of detection, and the application is wide. And the hardware algorithm can be used as an IP core, namely an intellectual property module, and is an authenticated and reusable IC module with certain determining function. The classification is soft IP (soft IP core), firm IP (firm IP core) and hard IP (hard IP core). Soft IP is a behavior of functional blocks described in some high-level language, but it is not related to what circuits and circuit elements are used to implement these behaviors, and an embodiment of the present application is soft IP. Algorithm chips are fabricated through semiconductor processes to be used in various nerve therapeutic devices. Compared with a software algorithm operated by the MCU, the power consumption is lower, and the integration level is higher.
Example 1
Referring to fig. 1, which is a schematic diagram illustrating steps of a method for automatically detecting epilepsy based on an area algorithm in an implantable closed loop system according to an embodiment of the present disclosure, it should be understood that the method is applied to an epilepsy detection device formed by a chip, and an execution body of the method may be an integrated circuit of the chip or an epilepsy detection device formed by the chip, and the detection method may include the following steps:
Step 102, configuring detection parameters through an SPI (serial peripheral interface), wherein the detection parameters at least comprise a detection window size, an interval window size and a background window size in one detection period.
It should be noted that, referring to fig. 2, a plurality of detection periods may be included in each detection scheme, where each detection period includes a background window, an interval window, and a detection window. Also, the background window may be located at a previous period of the detection window, and the interval window is located between the background window and the detection window. The first detection period shown in fig. 2 is from t0-t5, where t0-t2 is the background window, t2-t4 is the spacing window, t4-t5 is the detection window, and the second detection period is from t1-t6, where t1-t3 is the background window, t3-t5 is the spacing window, and t5-t6 is the detection window. The subsequent third detection period and fourth detection period are similar. As can be seen from fig. 2, except for the first detection period, each detection period overlaps with the previous detection period, that is, the bioelectric signal in the current detection period includes a part of the bioelectric signal in the previous detection period.
The size of the background window can be N1 times of the size of the detection window, the size of the interval window is N2 times of the size of the detection window, and both the N1 and the N2 are positive integers which are larger than or equal to 2. It should be understood that in the embodiment of the present disclosure, the values of N1 and N2 may be equal or unequal. After the size of the detection window is determined, the size of the background window and the size of the interval window can be flexibly configured according to the detection requirement of the round.
Step 104, acquiring bioelectric signals from the organism implanted with the detection chip in real time and storing the bioelectric signals in a memory.
In the embodiment of the present specification, the bioelectric signal may specifically be an electroencephalogram signal, or a deep brain electrophysiological signal, or a cortical bioelectric signal, or a central nervous signal, or the like.
The memory may be Sraml static random access memory having a capacity of 2kB for storing the bioelectric signals.
And 106, respectively calculating the absolute value of the difference between the amplitude value of the bioelectric signal and the DC offset according to the time sequence, and sending the absolute value to an accumulator.
Wherein the accumulator may be an accumulator that accumulates the area of each detection window. It should be appreciated that the ratio of the magnitude of the bioelectric signal to the DC offset described herein is essentially the ratio of the area encompassed by the magnitude of the bioelectric signal to the area encompassed by the DC offset.
And step 108, based on the value in the accumulator, respectively determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window.
Optionally, if the current detection window belongs to the first detection period of the current detection, when the counter counts from 1 to N1, writing the value in the accumulator into a background window register until the counter counts to N1, wherein the background window register stores the area sum of the background window corresponding to the current detection window, and when the counter counts to N1+N2+1, writing the value in the accumulator into a detection window register, wherein the detection window register stores the area sum of the current detection window.
Further, before counting, the method further comprises the steps of taking the detection window as a basic window unit, determining the number of basic window units in a background window, an interval window and a detection window which are contained in the first detection period as N1+N2+1, and correspondingly,
When the counting value of the counter is not more than N1+N2+1 and the sampling of the current window is finished, writing the numerical value of the memory into the buffer from a low address to a high address, adding 1 to the counter, when the counting value of the counter is less than or equal to N1 and the sampling of the current window is finished, sequentially accumulating and writing the numerical value into a background window register by the memory, wherein the background window register stores the area sum of the background window in a first detection period, and when the counting value of the counter is equal to N1+N2+1 and the sampling window is finished, writing the numerical value of the memory into a detection window register, and storing the area sum of the detection window in the first detection period by the detection window register.
Optionally, if the current detection window belongs to a non-first detection period of the current detection, reading the lowest-order value from the buffer, and writing the lowest-order value into a first temporary register for temporary storage;
The buffer shifts leftwards once, the numerical value of the memory is written into the N1+N2+1th address, and then the numerical value of the N1+1th bit in the buffer is read to a second temporary register for temporary storage;
writing the value in the memory into a detection window register so as to be convenient for reassigning the area and the value of the current detection window;
Subtracting the value of the first temporary register from the value of the background window register, and adding the value of the second temporary register to write the value of the first temporary register into the background window register as the area and assignment of the current background window.
Step 110, judging whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value.
The preset threshold value can be a standard value obtained according to an empirical data signal and is used for measuring whether an organism implanted with the detection chip suffers from epileptic symptoms or not.
And 112, outputting an identification signal representing the epileptic seizure to trigger the treatment device to perform stimulation treatment on the organism when the judgment result is larger than the judgment result.
In particular, the identification signal may be a Flag signal, and when the Flag signal is raised, it indicates that the seizure is triggered to perform stimulation treatment, otherwise, the seizure is not seizure, and further detection is continued.
The following describes the epilepsy detection scheme according to the present specification by way of specific examples. Taking an electroencephalogram signal as an example.
First, the algorithm signals and hardware units involved in the detection scheme are described.
The hardware algorithm has the input ports of CLK1 port, CLK2 port, nRST port, MOSI port, data-ADC port and EN-detect port, and the output port has MISO port and Flag port. The communication mode of the whole scheme adopts a serial peripheral interface SPI. The SPI communication mode is that data are sent on the rising edge, and data are collected on the falling edge. The CLK1 port and CLK2 port are two master clock ports of the same phase, respectively. The nRST port is a reset signal port, the signal output 0 is reset, and the signal output 1 works normally. The MOSI port is an SPI communication port, and signals are output to the host and input to the slave. The Data-ADC port is used for transmitting the brain electrical Data with the width of 16 bits, and can be connected with the output of the 16-bit ADC in practical application. The en_detect port is a detection enable signal port. The MISO port is an SPI communication port, and signals are input to and output from the host. The Flag port is a seizure warning signal port. Accordingly, each port corresponds to a respective signal output.
Further, define T is the number of detection window samples, configured through SPI ports, range 32-1024. N1 is a multiple of the size of the background window and the size of the detection window, and is configured through the SPI port, and ranges from 0 to 255. N2 is a multiple of the size of the interval window and the size of the detection window, and is configured through the SPI port, ranging from 0 to 255. LIM is a preset threshold, range 110-180, configured through SPI ports. CNT1 is a counter that counts the number of sampling points in the detection window. Q is a variable that counts the number of detection windows in the first calculation period. Sum1 is an accumulator for accumulating the area and the area within each detection window. Sum_background is a background window register for accumulating background window area and area. Sum_detect is a detection window register for storing the Sum of the areas of the detection windows in each calculation cycle. Sum_l is a first temporary register for storing the value of the lowest bit in the buffer. Sum_n is a second temporary register for storing the value of the N1+1 bit in the buffer. Sram1 is a static random access memory with a capacity of 2 kB. SramS1 is a buffer, dual port ram, for storing intermediate results during computation. SramS2 is an output buffer, a dual port ram, for buffering during epileptic electroencephalogram data output.
The detection scheme in the specification can roughly comprise the following parts:
1. The algorithm parameters are configured.
2. The brain electrical signal is stored to Sram1.
3. And (5) calculating the difference value between the amplitude of the brain electrical signal and the DC offset, and taking the absolute value.
4. And a calculation process for detecting whether epilepsy occurs. Wherein,
(4.1) First find the area of each detection window length and write the sum_background register, the area of the detection window and write the sum_detect register.
And (4.2) obtaining the average area of the detection window, obtaining the average area of the background window, judging the detection result and giving a Flag signal.
5. The process data for detecting epilepsy are output in series through the communication port.
The specific implementation process can refer to the following:
1. data_ADC [15:0] brain electrical Data was written to the Sram1 memory at a frequency of 200 Hz.
Wherein the input frequency can be flexibly set, 200Hz here being only an example.
2. The absolute value of the difference between the amplitude of the brain electrical signal and the DC offset is added to a Sum1 accumulator in sequence. The CNT1[10:0] counter counts the accumulated number of Sum1 from 0, taking the length of one detection window as one cycle.
3. The first calculation period, register Q records the number of detection windows in the first calculation period, Q > =0 and Q < =n1+n2+2. When Q < = n1+n2+1 and CNT1 = T (the current detection window ends and the next detection window adjacent thereto starts), the Sum1 value is written SramS from low address to high address, while Q is incremented by 1. When Q < = N1 and CNT1 = T, sum1 is sequentially accumulated and written into the sum_background register, which stores the area Sum of the background window in the first detection period, and when Q = N1+ N2+1 and CNT1 = T, sum1 is written into the sum_detect register, i.e. sum_detect register stores the detection window area Sum. According to the formula
Wherein sum_detect is the Sum of the areas of the detection windows, td is the number of the detection windows, default to 1, sum_background is the total Sum of the background windows, and T is the number of the background windows. The ratio of the average area of the detection window to the average area of the background window can be obtained, the ratio is compared with a preset threshold LIM, if the ratio is larger than the LIM, the epileptic seizure is indicated, meanwhile, the Flag signal is pulled high, and otherwise, the epileptic seizure is not indicated. The preset threshold LIM may be 400%, that is, when the ratio is less than or equal to 400%, the Flag signal is not triggered to be pulled high.
5. After the first detection is finished, a second detection period is entered.
At this time, sramS stores N1+n2+1 values, the data of the lower N1 bits is Sum1 value of the background window in the first calculation period, the data of the next N2 bits is Sum1 value of the interval window in the first calculation period, and the highest bit is Sum1 value of the detection window. When CNT 1=t, the lowest data in SramS is read out to the first temporary register sum_l for temporary storage, then SramS1 is shifted to the left once, sum1 value is written to the N1+n2+1 address, and then the N1+1 data in SramS1 is read out to the second temporary register sum_n for temporary storage, and at the same time Sum1 value is written to the register sum_detect. The current sum_background stores the area Sum of the background section in the first computing period, and at this time, sum_background-sum_l+sum_n is assigned to sum_background, namely the area Sum of the background window in the second computing period. And (3) obtaining the ratio of the average area of the detection interval of the second calculation period to the average area of the background window according to the formula (1), comparing the ratio with a threshold LIM, if the ratio is larger than the threshold LIM, indicating epileptic seizure, and pulling up the Flag signal, otherwise, indicating that the epileptic seizure is not seizure.
6. The loop of the third and further detection periods continues as per the logic in 5.
7. Every time seizure is detected, flag is pulled high, at which time the en_detect terminal can be pulled high externally, and suspension is detected and stimulation is performed.
Meanwhile, sram1 reading enabling is effective in a hardware algorithm, and through controlling an address end, 30s of electroencephalogram data before the seizure time and 60s of electroencephalogram data after the seizure time can be read. Because the reading frequency of the Sram1 is far smaller than the SPI communication frequency, data output by the Sram1 is written into the buffer SramS, and then sequentially read from low to high after the data is written, and the data is output in series through the MISO port, can be stored in external Flash after being output, can be used for subsequent medical research, and has great significance for the research of neuroscience.
Therefore, through the technical scheme, the bioelectric signals can be acquired and detected in real time by adopting a hardware algorithm, and then whether epileptic is sent to trigger stimulation treatment through the identification signal is determined according to the comparison of the ratio of the average area of the detection window to the average area of the background window in each detection period and the preset threshold value. Therefore, the integration level, the detection accuracy and the convenience are improved by low-power consumption real-time detection.
Example two
Referring to fig. 3, an apparatus 300 for automatically detecting epilepsy in an implantable closed loop system based on an area algorithm according to an embodiment of the present disclosure is shown, where the apparatus 300 mainly includes:
the configuration module 302 configures detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least comprise a detection window size, an interval window size and a background window size in one detection period;
the acquisition and storage module 304 acquires bioelectric signals from the organism implanted with the detection chip in real time and stores the bioelectric signals in a memory;
the calculating module 306 calculates the absolute value of the difference between the amplitude value of the bioelectric signal and the DC offset according to the time sequence and sends the absolute value to the accumulator;
A determining module 308, configured to determine, based on the values in the accumulator, an area sum of a current detection window and an area sum of a background window corresponding to the current detection window;
the detection module 310 is configured to determine whether a ratio of an average area of the current detection window to an average area of the background window is greater than a preset threshold;
The treatment module 312 outputs an identification signal representing the epileptic seizure to trigger the treatment device to perform stimulation treatment on the living body when the determination result is greater than.
Optionally, as an embodiment, the size of the background window is N1 times that of the detection window, and the size of the interval window is N2 times that of the detection window, where N1 and N2 are positive integers greater than or equal to 2.
In a specific implementation manner of the embodiment of the present specification, the determining module 308 is specifically configured to:
When the counter counts from 1 to N1, writing the value in the accumulator into a background window register until the counting to N1 is finished, wherein the background window register stores the area sum of a background window corresponding to the current detection window;
when the counter counts to N1+N2+1, the value in the accumulator is written into a detection window register, and the detection window register stores the area sum of the current detection window.
In yet another specific implementation of the embodiment of the present disclosure, before counting, the determining module 308 is further configured to:
the detection window is used as a basic window unit, the number of the basic window units N1+N2+1 in the background window, the interval window and the detection window contained in the first detection period are determined, and correspondingly,
When the counting value of the counter is not more than N1+N2+1 and the sampling of the current window is finished, writing the numerical value of the memory into the buffer from a low address to a high address, and adding 1 into the counter;
when the counting value of the counter is smaller than or equal to N1 and the sampling of the current window is finished, sequentially accumulating and writing the current window into a background window register by a memory, wherein the background window register stores the area sum of a background window in a first detection period;
when the count value of the counter is equal to N1+N2+1 and the current sampling window is over, the memory writes into a detection window register, and the detection window register stores the area sum of the detection window in the first detection period.
In another specific implementation manner of this embodiment of the present disclosure, if the current detection window belongs to a non-first detection period of the present round of detection, the determining module is specifically configured to:
Reading the lowest bit value from the buffer, and writing the lowest bit value into a first temporary register for temporary storage;
The buffer shifts leftwards once, the numerical value of the memory is written into the N1+N2+1th address, and then the numerical value of the N1+1th bit in the buffer is read to a second temporary register for temporary storage;
writing the value in the memory into a detection window register so as to be convenient for reassigning the area and the value of the current detection window;
Subtracting the value of the first temporary register from the value of the background window register, and adding the value of the second temporary register to write the value of the first temporary register into the background window register as the area and assignment of the current background window.
In still another specific implementation manner of the embodiments of the present disclosure, when the living body is stimulated for treatment, the apparatus further includes an output module, configured to obtain a bioelectric signal of a first period before the seizure timing and a bioelectric signal of a second period after the seizure timing, and serially output the bioelectric signals through the SPI.
Through the technical scheme, the bioelectric signals can be acquired and detected in real time by adopting a hardware algorithm, and then whether epileptic is sent to trigger stimulation treatment through the identification signal is determined according to the comparison of the ratio of the average area of the detection window to the average area of the background window in each detection period and the preset threshold value. Therefore, the integration level, the detection accuracy and the convenience are improved by low-power consumption real-time detection.
Example III
Referring to fig. 5, a schematic structural diagram of an apparatus for automatically detecting epilepsy based on an area algorithm in an implantable closed loop system according to an embodiment of the present disclosure at least includes a hardware algorithm chip and other functional modules for executing the method in the first embodiment, where the hardware algorithm chip includes a serial peripheral interface, a memory, an algorithm register, an accumulator, and a counter,
The serial peripheral interface is used for configuring detection parameters and transmitting bioelectric signals, wherein the detection parameters at least comprise detection window size, interval window size and background window size in one detection period;
The storage is respectively used for storing the bioelectric signals collected from the organism implanted with the detection chip in real time, and the absolute values of the differences between the amplitude values and the direct current bias of the bioelectric signals are respectively calculated according to the time sequence by the counter and then sent to the accumulator;
After the area sum of the current detection window and the area sum of the background window corresponding to the current detection window are respectively determined based on the value in the accumulator, a detection enabling end judges whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
and when the judging result is greater than the judging result, the serial peripheral interface outputs an identification signal representing epileptic seizure so as to trigger the treatment device to perform stimulation treatment on the organism.
The other hardware unit parts may refer to the content of the first embodiment, and are not described herein.
Example IV
Fig. 4 is a schematic structural view of an electronic device according to an embodiment of the present specification. Referring to fig. 4, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs, and a device for automatically detecting epilepsy is formed on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
Configuring detection parameters through an SPI (serial peripheral interface), wherein the detection parameters at least comprise a detection window size, an interval window size and a background window size in one detection period;
Collecting bioelectric signals from a living body implanted with the detection chip in real time and storing the bioelectric signals into a memory;
respectively calculating absolute values of the differences between the magnitudes of the bioelectric signals and the direct current bias according to time sequences, and sending the absolute values to an accumulator;
Based on the value in the accumulator, respectively determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window;
Judging whether the ratio of the average area of the current detection window to the average area of the background window is larger than a preset threshold value or not;
And when the judgment result is larger than the judgment result, outputting an identification signal representing the epileptic seizure so as to trigger the treatment device to perform stimulation treatment on the organism.
The method performed by the apparatus disclosed in the embodiment shown in fig. 1 of the present specification may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor including a central processing unit (Central Processing Unit, CPU), a network Processor (Network Processor, NP), etc., or may be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The methods, steps, and logic blocks disclosed in one or more embodiments of the present description may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in a hardware decoding processor or in a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute the method of fig. 1 and implement the functions of the corresponding apparatus in the embodiment shown in fig. 1, which is not described herein.
Of course, in addition to the software implementation, the electronic device of the embodiments of the present disclosure does not exclude other implementations, such as a logic device or a combination of software and hardware, that is, the execution subject of the following processing flow is not limited to each logic unit, but may also be hardware or a logic device.
Through the technical scheme, the bioelectric signals can be acquired and detected in real time by adopting a hardware algorithm, and then whether epileptic is sent to trigger stimulation treatment through the identification signal is determined according to the comparison of the ratio of the average area of the detection window to the average area of the background window in each detection period and the preset threshold value. Therefore, the integration level, the detection accuracy and the convenience are improved by low-power consumption real-time detection.
In summary, the foregoing description is only a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the protection scope of the present specification.
The systems, devices, modules, or units illustrated in one or more of the embodiments described above may be implemented in particular by a computer chip or entity, or by a product having some function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.