Disclosure of Invention
In order to solve the above technical problems, the present invention provides an automated diagnosis method for detecting heart abnormalities in real time, comprising:
Acquiring heart information of a heart in real time, wherein the heart information comprises a heartbeat interval signal, an initial intensity of sympathetic nerve activity and an initial intensity of parasympathetic nerve activity;
Setting a heart rhythmicity fluctuation monitoring model according to the heart information, and monitoring the rhythmicity fluctuation of the heart;
Setting a heart rate variability evolution model according to the heart information, and monitoring heart rate variability evolution of the heart;
Setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and carrying out diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Further, the heart rhythmic fluctuation monitoring model comprises:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
Further, the heart rate variability evolution model includes:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
Further, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Further, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
The invention also proposes an automated diagnostic system for detecting heart abnormalities in real time, comprising:
A heart information acquisition module for acquiring heart information of the heart in real time, wherein the heart information comprises a heartbeat interval signal, an initial intensity of sympathetic nerve activity and an initial intensity of parasympathetic nerve activity;
The rhythmicity fluctuation monitoring module is used for setting a heart rhythmicity fluctuation monitoring model according to the heart information to monitor rhythmicity fluctuation of the heart;
The heart rate variability evolution monitoring module is used for setting a heart rate variability evolution model according to the heart information to monitor heart rate variability evolution of the heart;
And the evaluation module is used for setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Further, the heart rhythmic fluctuation monitoring model comprises:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
Further, the heart rate variability evolution model includes:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
Further, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Further, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
By the technical scheme, the heart abnormality can be monitored and evaluated, so that the heart abnormality of a patient can be found in time.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment that can include one or more of a processor, a storage medium, and a display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, an embodiment of the present invention proposes an automated diagnostic method for detecting heart abnormalities in real time, including:
Step 101, acquiring heart information of a heart in real time, wherein the heart information comprises a heartbeat interval signal, initial intensity of sympathetic nerve activity and initial intensity of parasympathetic nerve activity;
102, setting a heart rhythmicity fluctuation monitoring model according to the heart information, and monitoring the rhythmicity fluctuation of the heart;
Heart rate variability, which reflects the state of balance of the autonomic nervous system (sympathetic and parasympathetic), is expressed by fluctuations in the time intervals (RR intervals) of the heart beat. This fluctuating pattern can be seen as a representation of a high-dimensional nonlinear system.
Modeling of a non-linear chaotic system assuming rhythmic fluctuations of the heart are driven by a non-linear chaotic system, the state of which can evolve over time and has a strong non-linear characteristic. We define this model as a high-dimensional system describing heart rhythms, modeled by a dynamic equation.
In particular, we use the heartbeat interval signalTo represent the change in cardiac rhythms, we can construct a new chaotic system that uses a phase space to describe the time-variability of each RR interval, the cardiac rhythmic fluctuation monitoring model includes:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring heart rate variability evolution of the heart;
Specifically, the present embodiment designs a new model to represent fluctuations in Heart Rate Variability (HRV). The model describes the heart rate variation by a kinetic equation based on a feedback mechanism. Assuming that there are two main influencing factors of the system, sympathetic and parasympathetic activities, this embodiment defines a new kinetic system to describe the effect of these influencing factors on heart rate fluctuations, which comprises for the heart rate variability evolution model:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
With respect to timeDynamic feedback variable at timeThe present embodiment is explained by the following example:
example 1 general health status
It is assumed that in a healthy state,The feedback adjustment of (2) is gradual, we set:
1. Is a periodic change (normal heart rhythm);
2. Initial state=(I.e., the initial feedback variable is equal to the initial heartbeat interval);
3. Feedback rate(Sixth adjustment factor of heart rate variability evolution model) is set to a smaller constant, assuming that=0.1。
Thus, the first and second substrates are bonded together,Will track smoothly over timeIs a variation of (c). For example, assume thatOscillating with a certain period, thenWill also adjust slowly to followIs a variation of (c).
Example 2 stress State
In the stress state, the sympathetic nerve activity increases and the heart rhythm changes more severely, we set:
1. the fluctuation amplitude of (a) increases reflecting that the heart rate accelerates or becomes irregular;
2. Initial state=But due to stress reaction, feedback rateIncrease, assume=0.5, Which means that the heart rhythm adjusts faster to the current state.
In this case the number of the elements to be formed is,The change in (a) will follow more rapidlyAnd the resulting fluctuation in heart rhythms may be more dramatic.
Example 3 parasympathetic Activity is enhanced (relaxed or sleep state)
In the case of increased parasympathetic activity, the heart rhythm tends to stabilize, the effect of the feedback mechanism is more pronounced, we set:
1. become smoother, less frequent (indicating a relaxed or sleep state);
2. Initial state=Feedback rate=0.05, Reflecting that the feedback process is slow.
At this time, the liquid crystal display device,Will adjust slower, following the smooth heart rhythm changes.
And 104, setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Specifically, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Specifically, the diagnosis and evaluation of the heart according to the heart abnormality evaluation value comprises the steps of setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
Example 2
As shown in fig. 2, an embodiment of the present invention further provides an automated diagnostic system for detecting heart abnormalities in real time, comprising:
A heart information acquisition module for acquiring heart information of the heart in real time, wherein the heart information comprises a heartbeat interval signal, an initial intensity of sympathetic nerve activity and an initial intensity of parasympathetic nerve activity;
The rhythmicity fluctuation monitoring module is used for setting a heart rhythmicity fluctuation monitoring model according to the heart information to monitor rhythmicity fluctuation of the heart;
specifically, the heart rhythmic fluctuation monitoring model comprises:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
The heart rate variability evolution monitoring module is used for setting a heart rate variability evolution model according to the heart information to monitor heart rate variability evolution of the heart;
Specifically, the heart rate variability evolution model includes:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
And the evaluation module is used for setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Specifically, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Specifically, the diagnosis and evaluation of the heart according to the heart abnormality evaluation value comprises the steps of setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the automatic diagnosis method for detecting heart abnormality in real time.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Optionally, in the present embodiment, the storage medium is arranged to store program code for performing the steps of, step 101, acquiring cardiac information of the heart in real time, wherein the cardiac information comprises a heartbeat interval signal, an initial intensity of sympathetic activity, an initial intensity of parasympathetic activity;
102, setting a heart rhythmicity fluctuation monitoring model according to the heart information, and monitoring the rhythmicity fluctuation of the heart;
specifically, the heart rhythmic fluctuation monitoring model comprises:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring heart rate variability evolution of the heart;
Specifically, the heart rate variability evolution model includes:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
And 104, setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Specifically, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Specifically, the diagnosis and evaluation of the heart according to the heart abnormality evaluation value comprises the steps of setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the automatic diagnosis method for detecting heart abnormality in real time.
In particular, the electronic device of the present embodiment may be a computer terminal that may include one or more processors and a storage medium.
The storage medium may be used to store a software program and a module, for example, in an automatic diagnosis method for detecting heart abnormalities in real time according to an embodiment of the present invention, and the processor executes various functional applications and data processing by running the software program and the module stored in the storage medium, that is, implements the above-mentioned automatic diagnosis method for detecting heart abnormalities in real time. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call the information and the application program stored in the storage medium through the transmission system to execute the following steps of step 101, acquiring heart information of the heart in real time, wherein the heart information comprises a heartbeat interval signal, initial intensity of sympathetic nerve activity and initial intensity of parasympathetic nerve activity;
102, setting a heart rhythmicity fluctuation monitoring model according to the heart information, and monitoring the rhythmicity fluctuation of the heart;
specifically, the heart rhythmic fluctuation monitoring model comprises:
,
Wherein,Is time ofA heartbeat interval signal at the time of the time,For a first adjustment factor of the heart rhythmic fluctuation monitoring model,A second adjustment factor for the heart rhythmic fluctuation monitoring model,A third adjustment factor for the heart rhythmic fluctuation monitoring model,A fourth adjustment factor for the heart rhythmic fluctuation monitoring model.
Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring heart rate variability evolution of the heart;
Specifically, the heart rate variability evolution model includes:
,
,
Wherein,For the initial intensity of the sympathetic activity,Is the decay factor of the sympathetic activity, is used for controlling the decay speed of the intensity of the sympathetic activity in a relaxed state,As a first adjustment factor of the heart rate variability evolution model,As a second adjustment factor of the heart rate variability evolution model,Is the initial phase of the intensity of the sympathetic activity, the phase shift for controlling the intensity of the sympathetic activity,Is time ofDynamic feedback variables at the time are used for simulating the adaptability change of the heart rhythm under different physiological states,For the initial intensity of parasympathetic activity,Is an attenuation coefficient of parasympathetic activity, is used for controlling the decay rate of the intensity of parasympathetic activity in a relaxed state,A third adjustment factor for the heart rate variability evolution model,For the fourth adjustment factor of the heart rate variability evolution model,For an initial phase of the intensity of parasympathetic activity, for controlling a phase shift of the intensity of parasympathetic activity,As a fifth adjustment factor of the heart rate variability evolution model,And the sixth adjustment factor of the heart rate variability evolution model.
And 104, setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to a heart rhythmicity fluctuation monitoring result and a heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.
Specifically, the heart abnormality assessment model includes:
,
Wherein,Is time ofAn evaluation value of heart abnormality at the time of the heart abnormality,A first adjustment factor for the cardiac abnormality assessment model,A second adjustment factor for the cardiac abnormality assessment model,A third adjustment factor for the cardiac abnormality assessment model,And a fourth adjustment factor for the cardiac abnormality assessment model.
Specifically, the diagnosis and evaluation of the heart according to the heart abnormality evaluation value comprises the steps of setting a heart abnormality threshold, and when the heart abnormality evaluation value exceeds the heart abnormality threshold, the heart is in an abnormal state.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer apparatus (which may be a personal computer, a server, or a network apparatus, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes a usb disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or a compact disk, etc. which can store the program code.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.