Automatic diagnosis method and system for detecting heart abnormity in real time

Abstract

The invention discloses an automatic diagnosis method and system for detecting heart abnormity in real time, and the method comprises the steps: obtaining the heart information of a heart in real time, the heart information comprising a heartbeat interval signal, the initial intensity of sympathetic nerve activity, and the initial intensity of parasympathetic nerve activity; setting a heart rhythmic fluctuation monitoring model according to the heart information, and monitoring the rhythmic fluctuation of the heart; according to the heart information, a heart rate variability evolution model is set, and heart rate variability evolution of the heart is monitored; and setting a heart abnormality evaluation model, calculating a heart abnormality evaluation value according to the rhythm fluctuation monitoring result of the heart and the heart rate variability evolution result of the heart, and performing diagnosis evaluation on the heart according to the heart abnormality evaluation value.

Description

Technical Field

[0001] The present invention belongs to the technical field of cardiac abnormality monitoring, and more specifically, relates to an automated diagnosis method and system for real-time detection of cardiac abnormalities. Background Art

[0002] Cardiac abnormality monitoring is an important part of modern medicine and health management, covering a variety of technologies and methods to monitor and diagnose heart health conditions, especially for the early detection and management of heart disease, arrhythmia, heart failure and other diseases. With the development of technology, the status of cardiac abnormality monitoring has become diversified, covering traditional clinical detection methods and modern wearable devices and remote monitoring systems.

[0003] Traditional methods of monitoring heart abnormalities are usually performed in hospitals and performed or analyzed by professional doctors. These methods include: Electrocardiogram (ECG / EKG): By placing electrodes on the chest, the waveform of the heart's electrical activity is recorded to help detect heart abnormalities such as arrhythmias and myocardial ischemia. Although ECG technology is mature, it usually requires specialized equipment and professionals.

[0004] Holter monitoring: This is a continuous monitoring method for 24 hours or longer, mainly used to detect some occasional heart abnormalities, especially arrhythmias. The ECG data is recorded in real time through a portable device.

[0005] Heart ultrasound (echocardiogram): Uses sound waves to examine the structure and function of the heart and detect conditions such as heart valve disease and heart failure.

[0006] Exercise test: By monitoring ECG changes during exercise, the heart's response to load is evaluated. It is usually used to detect coronary heart disease, heart function, etc.

[0007] However, traditional methods can only be diagnosed by professional doctors, and due to the different levels of doctors, inaccurate diagnostic results may occur for the same examination results. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes an automated diagnostic method for real-time detection of cardiac abnormalities, comprising: Acquire heart information of the heart in real time, wherein the heart information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, initial intensity of parasympathetic nerve activity; According to the heart information, a heart rhythm fluctuation monitoring model is set to monitor the heart rhythm fluctuation; According to the heart information, a heart rate variability evolution model is set to monitor the heart rate variability evolution of the heart; A heart abnormality assessment model is set, and a heart abnormality assessment value is calculated based on the heart rhythm fluctuation monitoring results and the heart rate variability evolution results, and a diagnosis and assessment of the heart is performed based on the heart abnormality assessment value.

[0009] Furthermore, the cardiac rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0010] Furthermore, the heart rate variability evolution model includes: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0011] Furthermore, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0012] Furthermore, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0013] The present invention also provides an automated diagnostic system for real-time detection of cardiac abnormalities, comprising: A cardiac information acquisition module, used to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; A rhythmic fluctuation monitoring module, used to set a cardiac rhythmic fluctuation monitoring model according to the cardiac information, and monitor the rhythmic fluctuation of the heart; A heart rate variability evolution monitoring module, used to set a heart rate variability evolution model according to the heart information, and monitor the heart rate variability evolution of the heart; The evaluation module is used to set up a heart abnormality evaluation model, calculate a heart abnormality evaluation value based on the heart's rhythmic fluctuation monitoring results and the heart rate variability evolution results, and perform a diagnostic evaluation on the heart based on the heart abnormality evaluation value.

[0014] Furthermore, the cardiac rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0015] Furthermore, the heart rate variability evolution model includes: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0016] Furthermore, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0017] Furthermore, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0018] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: The present invention can monitor and evaluate cardiac abnormalities through the above technical solutions, thereby timely discovering cardiac abnormalities of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0022] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0023] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0024] The display screen is used to display the user interface of each application.

[0025] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0026] Example 1 like Figure 1 As shown, the embodiment of the present invention provides an automated diagnostic method for real-time detection of cardiac abnormalities, comprising: Step 101, obtaining heart information of the heart in real time, wherein the heart information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; Step 102, according to the heart information, setting a heart rhythm fluctuation monitoring model to monitor the heart rhythm fluctuation; Relationship between heart rate variability and biological signals: Heart rate variability reflects the balance state of the autonomic nervous system (sympathetic and parasympathetic nerves), which is expressed by the fluctuation of the time interval (RR interval) of heart beats. This fluctuation pattern can be regarded as a manifestation of a high-dimensional nonlinear system.

[0027] Modeling of nonlinear chaotic system: Assuming that the rhythmic fluctuations of the heart are driven by a nonlinear chaotic system, the state of this system can evolve over time and has strong nonlinear characteristics. We define this model as a high-dimensional system that describes the heart rhythm, modeled by a dynamic equation.

[0028] Specifically, we use the heartbeat interval signal To represent the changes in heart rhythm, we can construct a new chaotic system and use a phase space to describe the time-varying nature of each RR interval. The heart rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0029] Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring the heart rate variability evolution of the heart; Specifically, this embodiment designs a new model to represent the fluctuation of heart rate variability (HRV). The model describes the change of heart rate through a dynamic equation based on a feedback mechanism. Assuming that there are two main influencing factors in the system: sympathetic nerve activity and parasympathetic nerve activity, this embodiment defines a new dynamic system to describe the effect of these influencing factors on heart rate fluctuations. The dynamic system is the heart rate variability evolution model including: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0030] About time Dynamic feedback variables , this embodiment is explained by the following examples: Example 1: General health status Assuming that in a healthy state, The feedback adjustment is smooth, we set: 1. It is a cyclical change (normal heart rhythm); 2. Initial state = (i.e. the initial feedback variable is equal to the initial heartbeat interval); 3. Feedback rate (the sixth adjustment factor of the heart rate variability evolution model) is set to a smaller constant, assuming =0.1.

[0031] therefore, Tracks smoothly over time For example, suppose Oscillates with a certain period, then It will also adjust slowly to follow changes.

[0032] Example 2: Stress Under stress, sympathetic nerve activity increases and heart rhythm changes more dramatically. We set: 1. Increased fluctuations in the heart rate indicate that the heart rate is accelerating or becoming irregular; 2. Initial state = , but due to the stress response, the feedback rate Increase, assuming =0.5, which means that the heart rhythm adapts to the current state faster.

[0033] in this case, The changes will follow more quickly Fluctuations in heart rhythm may cause more drastic changes.

[0034] Example 3: Increased parasympathetic activity (relaxation or sleep state) When the parasympathetic nerve activity is enhanced, the heart rhythm tends to be stable and the effect of the feedback mechanism becomes more obvious. We set: 1. Becomes more stable and has a lower frequency (indicating a state of relaxation or sleep); 2. Initial state = , feedback rate =0.05, reflecting that the feedback process is slow.

[0035] at this time, It adjusts more slowly, following the steady heart rhythm.

[0036] Step 104, setting a heart abnormality assessment model, and calculating a heart abnormality assessment value based on the heart rhythm fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnosis and assessment on the heart based on the heart abnormality assessment value.

[0037] Specifically, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0038] Specifically, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0039] Example 2 like Figure 2As shown, an embodiment of the present invention further provides an automated diagnostic system for real-time detection of cardiac abnormalities, comprising: A cardiac information acquisition module, used to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; A rhythmic fluctuation monitoring module, used to set a cardiac rhythmic fluctuation monitoring model according to the cardiac information, and monitor the rhythmic fluctuation of the heart; Specifically, the cardiac rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0040] A heart rate variability evolution monitoring module, used to set a heart rate variability evolution model according to the heart information, and monitor the heart rate variability evolution of the heart; Specifically, the heart rate variability evolution model includes: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0041] The evaluation module is used to set up a heart abnormality evaluation model, calculate a heart abnormality evaluation value based on the heart's rhythmic fluctuation monitoring results and the heart rate variability evolution results, and perform a diagnostic evaluation on the heart based on the heart abnormality evaluation value.

[0042] Specifically, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0043] Specifically, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0044] Example 3 The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the automated diagnosis method for real-time detection of cardiac abnormalities.

[0045] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0046] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining heart information of the heart in real time, wherein the heart information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, initial intensity of parasympathetic nerve activity; Step 102, according to the heart information, setting a heart rhythm fluctuation monitoring model to monitor the heart rhythm fluctuation; Specifically, the cardiac rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0047] Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring the heart rate variability evolution of the heart; Specifically, the heart rate variability evolution model includes: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0048] Step 104, setting a heart abnormality assessment model, and calculating a heart abnormality assessment value based on the heart rhythm fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnosis and assessment on the heart based on the heart abnormality assessment value.

[0049] Specifically, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0050] Specifically, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0051] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the automated diagnostic method for real-time detection of cardiac abnormalities.

[0052] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0053] Among them, the storage medium can be used to store software programs and modules, such as an automated diagnostic method for real-time detection of cardiac abnormalities in an embodiment of the present invention, corresponding program instructions / modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned automated diagnostic method for real-time detection of cardiac abnormalities. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0054] The processor can call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining heart information of the heart in real time, wherein the heart information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, initial intensity of parasympathetic nerve activity; Step 102, according to the heart information, setting a heart rhythm fluctuation monitoring model to monitor the heart rhythm fluctuation; Specifically, the cardiac rhythm fluctuation monitoring model includes: , in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

[0055] Step 103, setting a heart rate variability evolution model according to the heart information, and monitoring the heart rate variability evolution of the heart; Specifically, the heart rate variability evolution model includes: , , in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

[0056] Step 104, setting a heart abnormality assessment model, and calculating a heart abnormality assessment value based on the heart rhythm fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnosis and assessment on the heart based on the heart abnormality assessment value.

[0057] Specifically, the cardiac abnormality assessment model includes: , in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0058] Specifically, performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

[0059] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0060] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0061] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0062] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0063] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0064] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0065] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. An automated diagnostic method for real-time detection of cardiac abnormalities, characterized in that: include: Acquire heart information of the heart in real time, wherein the heart information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, initial intensity of parasympathetic nerve activity; According to the heart information, a heart rhythm fluctuation monitoring model is set to monitor the heart rhythm fluctuation; According to the heart information, a heart rate variability evolution model is set to monitor the heart rate variability evolution of the heart; A heart abnormality assessment model is set, and a heart abnormality assessment value is calculated based on the heart rhythm fluctuation monitoring results and the heart rate variability evolution results, and a diagnosis and assessment of the heart is performed based on the heart abnormality assessment value.

2. An automated diagnostic method for real-time detection of cardiac abnormalities as claimed in claim 1, characterized in that: The cardiac rhythm fluctuation monitoring model includes: ， in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

3. An automated diagnostic method for real-time detection of cardiac abnormalities as claimed in claim 2, characterized in that: The heart rate variability evolution model includes: ， ， in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

4. An automated diagnostic method for real-time detection of cardiac abnormalities as claimed in claim 3, characterized in that: The cardiac abnormality assessment model includes: ， in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

5. An automated diagnostic method for real-time detection of cardiac abnormalities as claimed in claim 4, characterized in that: Performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

6. An automated diagnostic system for real-time detection of cardiac abnormalities, characterized in that: include: A cardiac information acquisition module, used to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signal, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; A rhythmic fluctuation monitoring module, used to set a cardiac rhythmic fluctuation monitoring model according to the cardiac information, and monitor the rhythmic fluctuation of the heart; A heart rate variability evolution monitoring module, used to set a heart rate variability evolution model according to the heart information, and monitor the heart rate variability evolution of the heart; The evaluation module is used to set up a heart abnormality evaluation model, calculate a heart abnormality evaluation value based on the heart's rhythmic fluctuation monitoring results and the heart rate variability evolution results, and perform a diagnostic evaluation on the heart based on the heart abnormality evaluation value.

7. An automated diagnostic system for real-time detection of cardiac abnormalities as claimed in claim 6, characterized in that: The cardiac rhythm fluctuation monitoring model includes: ， in, For time The heartbeat interval signal is the first adjustment factor of the cardiac rhythm fluctuation monitoring model, is the second adjustment factor of the cardiac rhythm fluctuation monitoring model, is the third adjustment factor of the cardiac rhythm fluctuation monitoring model, It is the fourth adjustment factor of the cardiac rhythm fluctuation monitoring model.

8. An automated diagnostic system for real-time detection of cardiac abnormalities as claimed in claim 7, characterized in that: The heart rate variability evolution model includes: ， ， in, is the initial intensity of sympathetic nerve activity, is the attenuation coefficient of sympathetic nerve activity, which is used to control the decay rate of sympathetic nerve activity in a relaxed state. is the first adjustment factor of the heart rate variability evolution model, is the second adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the sympathetic nerve activity, the phase offset used to control the intensity of the sympathetic nerve activity, For time Dynamic feedback variables are used to simulate the adaptive changes of heart rhythm under different physiological states. is the initial intensity of parasympathetic nerve activity, is the attenuation coefficient of parasympathetic nerve activity, which is used to control the decay rate of the intensity of parasympathetic nerve activity in a relaxed state. is the third adjustment factor of the heart rate variability evolution model, is the fourth adjustment factor of the heart rate variability evolution model, is the initial phase of the intensity of the parasympathetic nerve activity, the phase shift for controlling the intensity of the parasympathetic nerve activity, is the fifth adjustment factor of the heart rate variability evolution model, It is the sixth adjustment factor of the heart rate variability evolution model.

9. An automated diagnostic system for real-time detection of cardiac abnormalities as claimed in claim 8, characterized in that: The cardiac abnormality assessment model includes: ， in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, The second adjustment factor for the cardiac abnormality assessment model, The third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

10. An automated diagnostic system for real-time detection of cardiac abnormalities as claimed in claim 9, characterized in that: Performing diagnostic evaluation on the heart according to the heart abnormality evaluation value includes: setting a heart abnormality threshold value, and when the heart abnormality evaluation value exceeds the heart abnormality threshold value, the heart is in an abnormal state.

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