An automated diagnostic method and system for real-time detection of cardiac abnormalities

Abstract

The present invention discloses an automated diagnostic method and system for real-time detection of cardiac abnormalities, the method comprising: acquiring cardiac information of the heart in real time, wherein the cardiac information comprises: a heartbeat interval signal, an initial intensity of sympathetic nerve activity, and an initial intensity of parasympathetic nerve activity; setting a cardiac rhythmic fluctuation monitoring model according to the cardiac information to monitor the rhythmic fluctuation of the heart; setting a heart rate variability evolution model according to the cardiac information to monitor the evolution of the heart rate variability of the heart; setting a cardiac abnormality evaluation model, and calculating a cardiac abnormality evaluation value according to the cardiac rhythmic fluctuation monitoring result and the heart rate variability evolution result, and performing a diagnostic evaluation on the heart according to the cardiac abnormality evaluation value.

Description

Technical Field

[0001] The present invention belongs to the technical field of cardiac anomaly monitoring, and more particularly, relates to an automated diagnostic method and system for real-time detection of cardiac anomalies. Background Art

[0002] Cardiac abnormality monitoring is an essential component of modern medicine and health management, encompassing a variety of technologies and methods for monitoring and diagnosing heart health, particularly for the early detection and management of conditions such as heart disease, arrhythmias, and heart failure. With technological advancements, cardiac abnormality monitoring has diversified, encompassing traditional clinical testing methods as well as 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:

[0004] Electrocardiogram (ECG / EKG): Electrodes are placed on the chest to record the waveform of the heart's electrical activity, helping to detect heart abnormalities such as arrhythmias and myocardial ischemia. Although ECG technology is mature, it usually requires specialized equipment and professionals.

[0005] Holter monitoring: This is a continuous monitoring method for 24 hours or longer, mainly used to detect occasional heart abnormalities, especially arrhythmias. ECG data is recorded in real time using a portable device.

[0006] Heart ultrasound (echocardiogram): This procedure uses sound waves to examine the structure and function of the heart and to detect conditions such as heart valve disease and heart failure.

[0007] Exercise test: By having the patient monitor electrocardiogram changes during exercise, the heart's response to load is evaluated. It is usually used to detect coronary heart disease, heart function, etc.

[0008] 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

[0009] To solve the above technical problems, the present invention proposes an automated diagnostic method for real-time detection of cardiac abnormalities, comprising:

[0010] Acquiring cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity;

[0011] According to the heart information, a heart rhythm fluctuation monitoring model is set to monitor the heart rhythm fluctuation;

[0012] According to the heart information, a heart rate variability evolution model is set to monitor the heart rate variability evolution of the heart;

[0013] A cardiac abnormality assessment model is set up, and a cardiac abnormality assessment value is calculated based on the cardiac rhythmic fluctuation monitoring results and the heart rate variability evolution results of the heart, and a diagnostic assessment of the heart is performed based on the cardiac abnormality assessment value.

[0014] Furthermore, the cardiac rhythm fluctuation monitoring model includes:

[0015] ,

[0016] 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.

[0017] Furthermore, the heart rate variability evolution model includes:

[0018] ,

[0019] ,

[0020] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0021] Furthermore, the cardiac abnormality assessment model includes:

[0022] ,

[0023] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0024] Furthermore, performing diagnostic evaluation on the heart according to the cardiac abnormality evaluation value includes: setting a cardiac abnormality threshold value, and when the cardiac abnormality evaluation value exceeds the cardiac abnormality threshold value, the heart is in an abnormal state.

[0025] The present invention also provides an automated diagnostic system for real-time detection of cardiac abnormalities, comprising:

[0026] A cardiac information acquisition module, configured to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity;

[0027] A rhythmic fluctuation monitoring module, configured to set a cardiac rhythmic fluctuation monitoring model based on the cardiac information and monitor the cardiac rhythmic fluctuation;

[0028] A heart rate variability evolution monitoring module, configured to set a heart rate variability evolution model based on the heart information and monitor the heart rate variability evolution of the heart;

[0029] The evaluation module is used to set a cardiac abnormality evaluation model, calculate a cardiac abnormality evaluation value based on the rhythmic fluctuation monitoring results of the heart and the heart rate variability evolution results of the heart, and perform a diagnostic evaluation on the heart based on the cardiac abnormality evaluation value.

[0030] Furthermore, the cardiac rhythm fluctuation monitoring model includes:

[0031] ,

[0032] 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.

[0033] Furthermore, the heart rate variability evolution model includes:

[0034] ,

[0035] ,

[0036] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0037] Furthermore, the cardiac abnormality assessment model includes:

[0038] ,

[0039] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0040] Furthermore, performing diagnostic evaluation on the heart according to the cardiac abnormality evaluation value includes: setting a cardiac abnormality threshold value, and when the cardiac abnormality evaluation value exceeds the cardiac abnormality threshold value, the heart is in an abnormal state.

[0041] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0042] The present invention can monitor and evaluate cardiac abnormalities through the above technical solutions, thereby timely discovering cardiac abnormalities in patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0044] Figure 2 This is a system structure diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0045] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] The method provided by the present invention can be implemented in the following terminal environment, wherein 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, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0047] A processor can include one or more processing cores. It connects various components within the terminal using various interfaces and circuits. It executes instructions, programs, code sets, or instruction sets stored in storage media, and accesses data stored in storage media to perform various terminal functions and process data.

[0048] 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.

[0049] The display is used to show the user interface of each application.

[0050] In addition, those skilled in the art will appreciate that the structure of the terminal described above does not limit the terminal. The terminal may include more or fewer components, or a combination of certain components, or a different arrangement of components. For example, the terminal may also include a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and other components, which will not be described in detail here.

[0051] Example 1

[0052] like Figure 1 As shown, an embodiment of the present invention provides an automated diagnostic method for real-time detection of cardiac abnormalities, comprising:

[0053] Step 101: 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;

[0054] Step 102: setting a cardiac rhythmic fluctuation monitoring model based on the cardiac information to monitor the cardiac rhythmic fluctuation;

[0055] The relationship between heart rate variability and biological signals: Heart rate variability reflects the balance of the autonomic nervous system (sympathetic and parasympathetic nerves). It is manifested by the fluctuation of the time interval between heart beats (RR interval). This fluctuation pattern can be regarded as the manifestation of a high-dimensional nonlinear system.

[0056] Nonlinear Chaotic System Modeling: We assume that the rhythmic fluctuations of the heart are driven by a nonlinear chaotic system whose state can evolve over time and has strong nonlinear characteristics. We define this model as a high-dimensional system that describes the cardiac rhythm and model it through a dynamic equation.

[0057] Specifically, we use the heartbeat interval signal To represent the changes in cardiac rhythm, we can construct a new chaotic system and use a phase space to describe the time-varying nature of each RR interval. The cardiac rhythm fluctuation monitoring model includes:

[0058] ,

[0059] 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.

[0060] Step 103: setting a heart rate variability evolution model based on the heart information to monitor the heart rate variability evolution;

[0061] Specifically, this embodiment designs a new model to represent fluctuations in heart rate variability (HRV). This model describes changes in heart rate using dynamic equations based on a feedback mechanism. Assuming two primary influencing factors in the system: sympathetic nerve activity and parasympathetic nerve activity, this embodiment defines a new dynamical system to describe the effects of these factors on heart rate fluctuations. This dynamical system, which represents the heart rate variability evolution model, includes:

[0062] ,

[0063] ,

[0064] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0065] About Time Dynamic feedback variables , this embodiment is explained through the following examples:

[0066] Example 1: General health status

[0067] Assuming a healthy state, The feedback adjustment is smooth, we set:

[0068] 1. The changes are cyclical (normal heart rhythm);

[0069] 2. Initial state = (i.e. the initial feedback variable is equal to the initial heartbeat interval);

[0070] 3. Feedback rate (the sixth adjustment factor of the heart rate variability evolution model) is set to a small constant, assuming =0.1.

[0071] therefore, Tracks smoothly over time For example, suppose Oscillates at a certain period, then It will also adjust slowly to follow changes.

[0072] Example 2: Stress

[0073] Under stress, sympathetic nerve activity increases and heart rhythm changes more dramatically. We set:

[0074] 1. Increased fluctuations in the heart rate indicate an increase in heart rate or irregularity.

[0075] 2. Initial state = However, due to the stress response, the feedback rate Increase, assuming =0.5, which means that the heart rhythm adapts to the current state faster.

[0076] in this case, The changes will follow more quickly Fluctuations in heart rhythm may cause more drastic changes.

[0077] Example 3: Increased parasympathetic nervous system activity (relaxation or sleep state)

[0078] When parasympathetic nerve activity is enhanced, the heart rhythm tends to be stable and the feedback mechanism becomes more effective. We set:

[0079] 1. Becomes more stable and has a lower frequency (indicating a state of relaxation or sleep);

[0080] 2. Initial state = , feedback rate =0.05, reflecting that the feedback process is slow.

[0081] at this time, It adjusts more slowly, following the steady heart rhythm.

[0082] Step 104 , setting a cardiac abnormality assessment model, and calculating a cardiac abnormality assessment value based on the cardiac rhythmic fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnostic assessment on the heart based on the cardiac abnormality assessment value.

[0083] Specifically, the cardiac abnormality assessment model includes:

[0084] ,

[0085] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0086] 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.

[0087] Example 2

[0088] like Figure 2 As shown, an embodiment of the present invention further provides an automated diagnostic system for real-time detection of cardiac abnormalities, comprising:

[0089] A cardiac information acquisition module, configured to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity;

[0090] A rhythmic fluctuation monitoring module, configured to set a cardiac rhythmic fluctuation monitoring model based on the cardiac information and monitor the cardiac rhythmic fluctuation;

[0091] Specifically, the cardiac rhythm fluctuation monitoring model includes:

[0092] ,

[0093] 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.

[0094] A heart rate variability evolution monitoring module, configured to set a heart rate variability evolution model based on the heart information and monitor the heart rate variability evolution of the heart;

[0095] Specifically, the heart rate variability evolution model includes:

[0096] ,

[0097] ,

[0098] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0099] The evaluation module is used to set a cardiac abnormality evaluation model, calculate a cardiac abnormality evaluation value based on the rhythmic fluctuation monitoring results of the heart and the heart rate variability evolution results of the heart, and perform a diagnostic evaluation on the heart based on the cardiac abnormality evaluation value.

[0100] Specifically, the cardiac abnormality assessment model includes:

[0101] ,

[0102] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0103] 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.

[0104] Example 3

[0105] An embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the automated diagnostic method for real-time detection of cardiac abnormalities.

[0106] Optionally, in this embodiment, the 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.

[0107] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining cardiac information of the heart in real time, wherein the cardiac information includes: a heartbeat interval signal, an initial intensity of sympathetic nerve activity, and an initial intensity of parasympathetic nerve activity;

[0108] Step 102: setting a cardiac rhythmic fluctuation monitoring model based on the cardiac information to monitor the cardiac rhythmic fluctuation;

[0109] Specifically, the cardiac rhythm fluctuation monitoring model includes:

[0110] ,

[0111] 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.

[0112] Step 103: setting a heart rate variability evolution model based on the heart information to monitor the heart rate variability evolution;

[0113] Specifically, the heart rate variability evolution model includes:

[0114] ,

[0115] ,

[0116] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0117] Step 104 , setting a cardiac abnormality assessment model, and calculating a cardiac abnormality assessment value based on the cardiac rhythmic fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnostic assessment on the heart based on the cardiac abnormality assessment value.

[0118] Specifically, the cardiac abnormality assessment model includes:

[0119] ,

[0120] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0121] 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.

[0122] Example 4

[0123] 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, which 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.

[0124] Specifically, the electronic device of this embodiment may be a computer terminal, which may include: one or more processors, and a storage medium.

[0125] The storage medium can be used to store software programs and modules, such as the corresponding program instructions / modules of an automated diagnostic method for real-time detection of cardiac abnormalities in an embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, thereby realizing the above-mentioned automated diagnostic method for real-time detection of cardiac abnormalities. The storage medium may include high-speed random access 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 located 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.

[0126] The processor may call information and applications stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity;

[0127] Step 102: setting a cardiac rhythmic fluctuation monitoring model based on the cardiac information to monitor the cardiac rhythmic fluctuation;

[0128] Specifically, the cardiac rhythm fluctuation monitoring model includes:

[0129] ,

[0130] 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.

[0131] Step 103: setting a heart rate variability evolution model based on the heart information to monitor the heart rate variability evolution;

[0132] Specifically, the heart rate variability evolution model includes:

[0133] ,

[0134] ,

[0135] 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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.

[0136] Step 104 , setting a cardiac abnormality assessment model, and calculating a cardiac abnormality assessment value based on the cardiac rhythmic fluctuation monitoring results and the heart rate variability evolution results, and performing a diagnostic assessment on the heart based on the cardiac abnormality assessment value.

[0137] Specifically, the cardiac abnormality assessment model includes:

[0138] ,

[0139] in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

[0140] 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.

[0141] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0142] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] 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 merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, 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, and can be electrical or other forms.

[0144] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0146] 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. The computer software product is stored in a storage medium and includes several instructions for enabling 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, etc. Various media that can store program codes.

[0147] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. An automated diagnostic method for real-time detection of cardiac abnormalities, characterized in that: include: Acquiring cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; According to the heart information, and using the heartbeat interval signal to represent the change of heart rhythm, a nonlinear chaotic heart rhythm fluctuation monitoring model is set to monitor the rhythm fluctuation of the heart; 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; According to the heart information, a heart rate variability evolution model is set to monitor the heart rate variability evolution of the heart; 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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; A cardiac abnormality assessment model is set up, and a cardiac abnormality assessment value is calculated based on the cardiac rhythm fluctuation monitoring results and the heart rate variability evolution results, and a diagnostic assessment of the heart is performed based on the cardiac abnormality assessment value. The cardiac abnormality assessment model includes: ， in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

2. An automated diagnostic method for real-time detection of cardiac abnormalities according to claim 1, characterized in that: Performing a 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.

3. An automated diagnostic system for real-time detection of cardiac abnormalities, characterized in that: include: A cardiac information acquisition module, configured to acquire cardiac information of the heart in real time, wherein the cardiac information includes: heartbeat interval signals, initial intensity of sympathetic nerve activity, and initial intensity of parasympathetic nerve activity; A rhythmic fluctuation monitoring module is used to set a nonlinear chaotic cardiac rhythmic fluctuation monitoring model based on the cardiac information and using a heartbeat interval signal to represent changes in cardiac rhythm, so as to monitor the rhythmic fluctuation of the heart; 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; A heart rate variability evolution monitoring module, configured to set a heart rate variability evolution model based on the heart information and monitor the heart rate variability evolution of the heart; 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, and is the phase offset for controlling 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 nervous system 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, and is the phase offset 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; The evaluation module is used to set a cardiac abnormality evaluation model, calculate a cardiac abnormality evaluation value based on the cardiac rhythm fluctuation monitoring results and the heart rate variability evolution results, and perform a diagnostic evaluation on the heart based on the cardiac abnormality evaluation value. The cardiac abnormality assessment model includes: ， in, For time Heart abnormality assessment value, The first adjustment factor for the cardiac abnormality assessment model, is the second adjustment factor for the cardiac abnormality assessment model, is the third adjustment factor for the cardiac abnormality assessment model, A fourth adjustment factor for the model was assessed for cardiac abnormalities.

4. An automated diagnostic system for real-time detection of cardiac abnormalities according to claim 3, characterized in that: Performing a 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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