Cardio-cerebral health monitoring interpretability classification method based on multi-stage electrocardiogram

Abstract

The invention discloses an interpretability classification method for heart and brain health monitoring based on a multi-stage electrocardiogram, and belongs to the field of disease early screening, and the method comprises the following steps: building a prospective queue based on a containing and arranging standard, the prospective queue being a reference population; collecting multi-stage electrocardiogram data of a reference population to establish a reference sample of the high-quality electrocardiogram data; obtaining a multi-stage electrocardiogram variable based on the reference sample; creating an individual profile of a reference population based on the multi-stage electrocardiogram variables to obtain a reference space; calculating the dissimilarity between the to-be-classified individuals and the reference crowd to obtain the individual general situation of the to-be-classified individuals; and projecting the individual general situation of the to-be-classified individual to a reference space to determine a classification result. According to the method, the steady classification of the patient state is realized by mapping the subtle change of the electrocardiogram variable to the crowd level.

Description

Technical Field

[0001] The present invention belongs to the technical field of early disease screening, and in particular relates to an explainable classification method for heart and brain health monitoring based on a multi-stage electrocardiogram. Background Art

[0002] In the fast-paced and high-pressure environment of modern society, the mental stress on individuals has increased significantly. Long-term chronic stress not only poses a challenge to mental health, but also has a profound impact on physical health. Under the influence of chronic stress, the regulation of the autonomic nervous system may be disturbed, leading to cardiovascular dysfunction such as increased blood pressure. This long-term cardiovascular system stress may increase the risk of myocardial ischemia and may even trigger acute myocardial infarction, causing serious health and economic burdens on individual patients as well as society and families.

[0003] At present, society lacks awareness and attention to individual emotion regulation ability and mental health. The public's understanding of mental health is often limited to extreme mental illnesses, while ignoring the potential impact of chronic stress on emotions and behaviors. This neglect may lead to the exacerbation of the problem and even, in some cases, may evolve into a serious mental health crisis. In recent years, the concepts of the "heart-brain axis" and "brain-heart axis" have gradually revealed the bidirectional regulatory relationship between the heart and the brain, emphasizing the importance of paying attention to both mental and heart health in research and practice.

[0004] The electrocardiogram (ECG) is a convenient and economical health monitoring tool that is widely used in various fields from primary health care to family health monitoring. It is a preferred screening tool for assessing physical and mental health. Despite this, current ECG-based algorithms mainly focus on screening for diseases such as arrhythmias, while ignoring the potential of ECG in terms of mental state. Heart rate variability (HRV), as an ECG-derived indicator, is a biomarker that reflects the regulation of the autonomic nervous system. It has been confirmed by a large number of literatures that it can reflect an individual's mental state; and the widely used waveform characteristics of the ECG can reflect cardiac activity. In summary, the ECG is a powerful tool for long-term monitoring of patients' physical and mental health.

[0005] However, the application of ECG in the field of cardiovascular and brain health monitoring still faces the following challenges: (1) Most existing electrocardiogram-based studies do not record the corresponding mental state of the subjects, and the research faces the problem of insufficient data; (2) There are large differences in individual responses to mental stress stimuli, and ECG signals during mental stress are more easily affected by environmental and patient noise, which increases the difficulty of robust variable analysis.

[0006] Therefore, establishing a corresponding reference cohort and developing a robust ECG analysis method that is insensitive to interference and bias are crucial for cardio-cerebral health monitoring. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes an explainable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram to solve the problems existing in the above-mentioned prior art.

[0008] To achieve the above objectives, the present invention provides an explainable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram, comprising:

[0009] Establish a prospective cohort based on the inclusion criteria, and the prospective cohort is the reference population;

[0010] Collecting multi-stage electrocardiogram data of the reference population to establish a reference sample of high-quality electrocardiogram data;

[0011] Obtaining multi-stage electrocardiogram variables based on the reference sample;

[0012] Creating individual profiles of a reference population based on the multi-stage electrocardiogram variables to obtain a reference space;

[0013] Calculating the dissimilarity between the individual to be classified and the reference population to obtain an individual profile of the individual to be classified;

[0014] The individual profile of the individual to be classified is projected onto the reference space to determine a classification result.

[0015] Optionally, the reference population includes patients with angina pectoris, subjects with high mental stress, and a healthy control group.

[0016] Optionally, the process of creating a reference sample of high quality ECG data includes:

[0017] The reference population was continuously monitored to obtain electrocardiograms;

[0018] Extracting label information and reducing noise from the electrocardiogram to obtain standardized electrocardiogram data;

[0019] A reference sample of high-quality electrocardiogram data of a reference population is obtained based on the standardized electrocardiogram data.

[0020] Optionally, the calculation process of obtaining multi-stage electrocardiogram variables based on the reference sample includes:

[0021] Dividing the reference sample into a plurality of stages, wherein the plurality of stages include: a rest period, a stress period, and a recovery period;

[0022] The ECG variables of each stage were calculated independently to obtain several within-stage variables;

[0023] By calculating the values ​​between consecutive stages through directional subtraction, several inter-stage difference variables are obtained;

[0024] Based on several intra-stage variables and inter-stage difference variables, 88 electrocardiogram-based interpretable variables were obtained.

[0025] Optionally, the process of creating an individual profile of a reference population based on the multi-stage electrocardiogram variables and classifying the individuals to be classified based on the individual profile of the reference population includes:

[0026] Based on the multi-stage ECG variables of the reference population, the corresponding variable set is calculated for each stage and the variable set is vectorized to obtain several individual vectors;

[0027] Calculate the dissimilarity between any two individual vectors to generate an individual profile file for each member of the reference population, and construct a reference space based on the individual profile files of several members;

[0028] Calculating the dissimilarity between the individual to be classified and the reference population to obtain an individual profile of the individual to be classified;

[0029] Projecting the individual profile of the individual to be classified into the reference space to determine the classification result;

[0030] Among them, the Pearson distance coefficient is used to quantify the dissimilarity between any two individual vectors. The expression for quantifying the dissimilarity between any two individual vectors is:

[0031] d(x i ,x j )=1-r(x i ,x j )

[0032] In the formula, represents the i-th individual, d(x i , x j ) represents individual x i and x j The distance between them, r(x i , x j ) represents the Pearson correlation coefficient.

[0033] Optionally, the process of projecting the individual profile of the individual to be classified into the reference space to obtain a classification result includes:

[0034] Projecting the individual profile file of the individual to be classified into a specific reference space constructed from the individual profiles of the reference cohort;

[0035] The category to which the individual to be classified belongs is determined by minimizing the mean distance, and the constructed effect is visualized using the multidimensional scaling projection method.

[0036] The present invention also provides a computer terminal device, comprising:

[0037] one or more processors;

[0038] A memory, coupled to the processor, for storing one or more programs;

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement an explainable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram.

[0040] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an explainable classification method for cardio-cerebral health monitoring based on a multi-stage electrocardiogram is implemented.

[0041] Compared with the prior art, the present invention has the following advantages and technical effects:

[0042] The present invention converts the microscopic changes of multiple variables of patients in electrocardiogram monitoring into distance metrics at the population level. This method significantly reduces the data disturbance caused by differences in individual responses to mental stimulation and the influence of noise during data acquisition. By mapping subtle changes in electrocardiogram variables to the population level, a robust classification of patient status is achieved. This method with anti-interference ability provides a solid technical foundation for applying the technology of the present invention to daily wearable devices such as smart bracelets and smart watches. By integrating this method in these devices, real-time monitoring and analysis of user health status can be achieved, thereby providing users with timely health warnings and suggestions.

[0043] This invention maps the subtle changes of multiple variables in multi-stage electrocardiograms to the population level, greatly reducing the impact of data bias (individual heterogeneity) and data noise (instability in signal acquisition) on classification results, and provides a new and interpretable classification method for cardiovascular and cerebrovascular health monitoring. This method not only improves the accuracy of classification, but also enhances the interpretability of the results, providing a powerful tool for early disease screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 It is the reference queue data collection of the embodiment of the present invention;

[0046] Figure 2 ECG variable computer profile algorithm construction for embodiments of the present invention;

[0047] Figure 3 It is the construction of the overview algorithm and visualization of the effect of the embodiment of the present invention;

[0048] Figure 4 An iterative method for reference space in the overview algorithm of an embodiment of the present invention;

[0049] Figure 5 This is the verification and application of the overview algorithm of the embodiment of the present invention. DETAILED DESCRIPTION

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] Embodiment 1

[0053] like Figure 1 As shown, this embodiment provides an explainable classification method for heart and brain health monitoring based on multi-stage electrocardiogram, including the following steps:

[0054] This embodiment uses multi-stage ECG data to capture the response of individuals under different mental stress based on group distance analysis. These data include statistically significant HRV and waveform variables during the rest period, stimulation period, and recovery period, as shown in Table 1. By integrating and converting these variables into a feature set that reflects individual differences, this embodiment can construct a unique individual feature profile.

[0055] Table 1

[0056]

[0057]

[0058]

[0059]

[0060] The core of this method is to map the microscopic changes of ECG variables to the macroscopic "distance" of the population, thereby quantifying the physiological and psychological state of individuals at the population level. In addition, this method achieves a preliminary classification of the individual's mental state and the presence of myocardial ischemia by comparing the individual feature profile with the reference space constructed by the reference sample. This method effectively reduces the impact of fluctuations in individual physiological variables and improves the accuracy of classification.

[0061] As a specific implementation of this embodiment, the method for constructing such a feature profile relies on establishing a well-representative reference sample of myocardial ischemia induced by mental stress. In order to ensure that the method can be widely used in the assessment of mental health status and early warning of myocardial ischemia, this embodiment constructs a diversified cohort, covering a normal control group, stress patients assessed by multiple mental stress scales, and patients with myocardial ischemia induced by mental stress.

[0062] To ensure the broad representativeness of the sample and reduce the heterogeneity of the data, this example performed strict age matching when the subjects were enrolled. All cohorts used a unified standardized screening test to ensure the consistency of data collection. The subjects were arranged to receive mental stimulation in a specific time period from 7:00 to 8:00 in the morning, and the collection of electrocardiogram data was completed at the same time. This arrangement is intended to minimize the impact of circadian rhythm on the data and ensure the consistency and comparability of the collected data.

[0063] The effectiveness of this method has been demonstrated through verification on a test cohort. This embodiment provides a reference data set and a robust monitoring algorithm, providing a new tool for monitoring the physical and mental health of the population. These results are expected to be integrated into wearable devices such as bracelets and smart watches to achieve real-time monitoring of individual health status and timely warning, thereby improving people's quality of life and health level.

[0064] As a specific implementation of this embodiment, the exclusion standard adopted in this embodiment is as follows:

[0065] Inclusion criteria: 1) women aged between 18 and 75 years; 2) with chest pain or other angina symptoms; 3) without obstructive coronary artery disease (coronary artery stenosis less than 50%); 4) with self-reported depression and other problems.

[0066] Exclusion criteria: 1) chest pain caused by diseases other than the cardiovascular system; 2) complications of other serious diseases, such as pulmonary embolism, serious arrhythmia or aortic dissection; 3) complications of severe valvular disease; 4) New York Heart Association functional classification IV; 5) myocardial infarction with coronary artery obstruction in the past month; 6) apical ballooning syndrome in the past month; 7) substance abuse, such as alcohol and / or illegal drugs, use of antidepressants and / or antianxiety drugs within 4 weeks before study registration; 8) participation in other drug trials in the past 3 months; complications of severe mental illness; 9) currently pregnant or breastfeeding; 10) currently using postmenopausal hormone therapy and currently using psychotropic drugs.

[0067] Potential participants were identified by the clinical care team and initial discussions were held with potential participants, and those who expressed interest in participating in the study were asked to provide written consent.

[0068] As a specific implementation method of this embodiment, the following steps are included: establishing a prospective cohort based on the inclusion and exclusion criteria, the prospective cohort is a reference population; collecting multi-stage electrocardiogram data of the reference population to establish a reference sample of high-quality electrocardiogram data; calculating based on the reference sample to obtain multi-stage electrocardiogram variables; creating an individual profile of the reference population based on the multi-stage electrocardiogram variables to obtain a reference space; calculating the dissimilarity between the individual to be classified and the reference population to obtain the individual profile of the individual to be classified; and projecting the individual profile of the individual to be classified to the reference space to determine the classification result.

[0069] (1) Furthermore, the cohort was constructed to include the following three groups of people:

[0070] a) Subjects aged 18-75 years with angina pectoris but without organic heart changes.

[0071] b) Subjects aged 18-75 years with higher than normal levels of mental stress (For psychological assessment, the Hospital Anxiety and Depression Scale (HADS) was used to measure depressive symptoms, which contains different subscales for assessing anxiety (HADS-A) and depression (HADS-D). Emotional state was assessed by the Positive and Negative Affect Scale (PANAS), which distinguishes between positive (PANAS-P) and negative emotions (PANAS-N). The State-Trait Anxiety Inventory (STAI) provided insights into the levels of current (state) anxiety (STAI-S) and general (trait) anxiety (STAI-T). In addition, participants completed the Perceived Stress Scale (PSS), the Life Events Scale (LES), and the Post-Traumatic Stress Disorder (PTSD) Checklist-Civilian Version (PCL-C) to provide a comprehensive overview of their psychological state.)

[0072] c) Healthy controls age-matched to a and b.

[0073] (2) Furthermore, the data collection process includes: using a standard 12-lead ECG system to continuously monitor the reference population to obtain an ECG; performing signal filtering and label information extraction on the ECG to obtain ECG features; performing standardization processing on the ECG features to obtain standardized ECG features; and obtaining a reference sample of high-quality ECG data of the reference population based on the ECG features.

[0074] Furthermore, this embodiment uses a standard 12-lead ECG system and uses Tim software provided by Beijing, China for continuous monitoring. The system has 16-bit accuracy and a sampling frequency of 500 Hz. The ECG recording starts with the participant in a resting state and continues for an additional six minutes after the completion of different mental stress environments (three mental tasks such as Stroop test, angry recall speech, and timed mental arithmetic). Figure 1As shown. The MedEx MECG-200 ECG analysis system was used to filter the signal and extract label information. Noise removal from power supply interference, baseline drift, and muscle contraction was carefully performed by using two median filters (200 ms, 600 ms) and Daubechies wavelets. These extracted features were normalized by z-score normalization. Myocardial ischemia-related labels were determined by three senior clinicians based on SDS ≥ 3 in PET / CT imaging during mental stimulation (clinical gold indicator).

[0075] (3) Furthermore, the multi-stage ECG variable calculation process includes: dividing the reference sample into several stages, including: rest period, stress period and recovery period, independently calculating the ECG variables of each stage to obtain several intra-stage variables; obtaining several inter-stage difference variables by directional subtraction of consecutive stage values; and obtaining 88 ECG-based interpretable variables based on several intra-stage variables and inter-stage difference variables.

[0076] Furthermore, for the ECG variables, a detailed literature review was conducted to identify 88 ECG-based explanatory variables, including ECG waveform variables and heart rate variability variables. The ECG records were exported and segmented into three different phases corresponding to the timeline of the mental stress task: rest period (6 minutes), stress period (12 minutes), and recovery period (6 minutes), as shown in Figure 2 The ECG variables were calculated independently for each phase, resulting in a total of 264 within-phase variables (88 for each phase). In addition, another set of between-phase difference variables was derived by directional subtraction of values ​​between consecutive phases (stress-rest, recovery-stress, rest-recovery), for a total of 264 variables (88 in each direction).

[0077] (4) Further, individual profile algorithm description

[0078] This example proposes a distance-based approach to construct inter-group differentiation features using ECG data, called "individual profiles". The approach includes two main steps:

[0079] First, a specific reference space for the above three populations is established. Based on the multi-stage ECG variables of the reference cohort, and by calculating the dissimilarity between the individual and other individuals in the multi-stage ECG variables, an individual profile file of each member is generated. The projection of individual profile files of the same category forms the reference space of the category. In the verification data of this embodiment, the number of categories is 3. Secondly, by evaluating the differences between the individuals to be classified and the members of the reference cohort, profile files of the individuals to be classified are created. These profile files are then projected into the constructed reference space, and the classification results are determined by their positions in the reference space, such as Figure 2 shown.

[0080] In the profile construction, from the data set D = {x1, x2, ..., x N}, where N represents the size of the dataset and {1, 2, ..., C} represents the patient labels. Based on the statistically significant ECG variables from within-stage, between-stage, and their aggregate sets, a vector containing the within-stage and between-stage variables was created for each individual. Subsequently, the Pearson distance coefficient was used to quantify the differences between these vectors, calculated as:

[0081] d(x i ,x j )=1-r(x i ,x j )

[0082] in represents the i-th patient, c i represents the label of the i-th patient, d(x i , x j )

[0083] Represents patient x i and x j The distance between them, r(x i , x j ) represents the Pearson correlation coefficient, which is defined as follows:

[0084]

[0085] in represents the vth variable of the ith patient, V represents the length of the reduced variable, and Represents patient x i and x j The mean over all variables.

[0086] In the present invention, the study cohort is used as the dataset D. Using all individual profile files of the study cohort, the interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram establishes a specific reference space through MDS projection, and then performs the classification task on the test cohort. For a new sample x t The goal of this embodiment is to determine its k The labels in the (k=1, 2, ..., C) categories are classified by minimizing the mean distance, which is calculated from the distances between samples.

[0087]

[0088] Among them, C * Represents a new sample x * Category, N kIndicates category c k The number of samples in d(x i ,x t ) represents the sample x i With x t The distance between.

[0089] Figure 3 The details of the profile algorithm are demonstrated, including the integration of intra-stage variables and inter-stage variables. During the research, the interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiograms performed two-dimensional MDS projections on the profiles constructed by intra-stage and inter-stage variables and the fusion of the two, respectively. It was proved that the profile constructed by combining intra-stage and inter-stage variables has a better effect of narrowing the distance within the class and widening the distance between classes. Figure 3 C in Figure 2 shows that using only the original ECG variables results in randomly scattered data points without forming clear groups. However, when applying intra-stage or inter-stage individual profiles, samples in the same group are clustered more tightly, while samples in different groups are more separated. This effect of clearer grouping is more obvious in the projection of the individual profiles constructed by the integrated vector. As shown in Table 2, the performance of the individual profile method in the test set (n=53).

[0090] Table 2

[0091]

[0092] like Figure 4 As shown in the figure, the actual application process of individual profiles is: (i) expanding the reference sample size through screening tests; (ii) iteratively optimizing the reference space until the centroids of the corresponding categories no longer change significantly (reaching a certain threshold), indicating that a basic stable state has been reached; (iii) projecting the new individual profile files into the reference space for preliminary classification based on the group centroid distance.

[0093] Furthermore, this example further evaluates the robustness of the analysis method. First, this example cuts and splices data from the study cohort. The enhanced dataset is divided into 20 subsets. By gradually increasing the sample size, it is confirmed that when the sample size is large enough, the deviation of the group center achieves stability, such as Figure 5 As shown, part A in the figure is a schematic diagram of reference space iteration, and part B is Control, MSIMI (-) and MSIMI (+) category centers. During the data iteration process, the movement gradually decreases and reaches stability. This confirms the scalability of the present invention: that is, when the number of people in the reference space is increased, the present invention will have higher robustness; and by incorporating more common heart and brain diseases, the types of diseases in the reference space can be increased, expanding the scope of application of the method, thereby realizing daily monitoring of a variety of common heart and brain diseases.

[0094] The present invention collects electrocardiogram (ECG) data of subjects under mental stress under strictly controlled experimental conditions. This step effectively fills the data gap in existing research and provides valuable original data for subsequent analysis.

[0095] Furthermore, as a specific implementation of this embodiment, the present invention aims to promote early screening or routine monitoring of cardiocerebral diseases, making it essential to the applicability of single-lead data of wearable devices. Therefore, this embodiment calculates the Spearman rank correlation coefficient between the single-lead curve and the 12-lead curve of the test cohort, and evaluates the performance of each ECG lead. As shown in Table 3, although the Spearman rank correlation coefficient of the single-lead curve shows a slight change compared with the 12-lead curve, it does not affect the classification results. This shows that the analysis method may be applicable to single-lead data from wearable devices.

[0096] Table 3

[0097]

[0098] Wherein, ρ represents the Spearman rank correlation coefficient, Sen represents sensitivity, Spe represents specificity, and Acc represents sensitivity.

[0099] Embodiment 2

[0100] This embodiment also provides a computer terminal device, including:

[0101] one or more processors;

[0102] A memory, coupled to the processor, for storing one or more programs;

[0103] When one or more programs are executed by one or more processors, the one or more processors implement an explainable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram.

[0104] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, an explainable classification method for heart and brain health monitoring based on a multi-stage electrocardiogram is implemented.

[0105] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram, characterized in that: The following steps are involved: Establish a prospective cohort based on the inclusion criteria, and the prospective cohort is the reference population; Collecting multi-stage electrocardiogram data of the reference population to establish a reference sample of high-quality electrocardiogram data; Obtaining multi-stage electrocardiogram variables based on the reference sample; Creating individual profiles of a reference population based on the multi-stage electrocardiogram variables to obtain a reference space; Calculating the dissimilarity between the individual to be classified and the reference population to obtain an individual profile of the individual to be classified; The individual profile of the individual to be classified is projected onto the reference space to determine a classification result.

2. The interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram according to claim 1 is characterized in that: The reference population includes patients with angina pectoris, subjects with high mental stress, and a healthy control group.

3. The interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram according to claim 1 is characterized in that: The process of creating a reference sample of high-quality ECG data includes: The reference population was continuously monitored to obtain electrocardiograms; Extracting label information and reducing noise from the electrocardiogram to obtain standardized electrocardiogram data; A reference sample of high-quality electrocardiogram data of a reference population is obtained based on the standardized electrocardiogram data.

4. The interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram according to claim 1 is characterized in that: The calculation process of obtaining multi-stage ECG variables based on the reference sample includes: Dividing the reference sample into a plurality of stages, wherein the plurality of stages include: a rest period, a stress period, and a recovery period; The ECG variables of each stage were calculated independently to obtain several within-stage variables; By calculating the values ​​between consecutive stages through directional subtraction, several inter-stage difference variables are obtained; Based on several intra-stage variables and inter-stage difference variables, 88 electrocardiogram-based interpretable variables were obtained.

5. The interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram according to claim 1 is characterized in that: The process of creating an individual profile of a reference population based on the multi-stage electrocardiogram variables and classifying the individuals to be classified based on the individual profile of the reference population includes: Based on the multi-stage ECG variables of the reference population, the corresponding variable set is calculated for each stage and the variable set is vectorized to obtain several individual vectors; Calculate the dissimilarity between any two individual vectors to generate an individual profile file for each member of the reference population, and construct a reference space based on the individual profile files of several members; Calculating the dissimilarity between the individual to be classified and the reference population to obtain an individual profile of the individual to be classified; Projecting the individual profile of the individual to be classified into the reference space to determine the classification result; Among them, the Pearson distance coefficient is used to quantify the dissimilarity between any two individual vectors. The expression for quantifying the dissimilarity between any two individual vectors is: d(x i ,x j )=1-r(x i ,x j ) In the formula, represents the i-th individual, d(x i , x j ) represents individual x i and x j The distance between them, r(x i , x j ) represents the Pearson correlation coefficient.

6. The interpretable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram according to claim 5 is characterized in that: The process of projecting the individual profile of the individual to be classified into the reference space to obtain the classification result includes: Projecting the individual profile file of the individual to be classified into a specific reference space constructed from the individual profiles of the reference cohort; The category to which the individual to be classified belongs is determined by minimizing the mean distance, and the constructed effect is visualized using the multidimensional scaling projection method.

7. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the explainable classification method for cardio-cerebral health monitoring based on multi-stage electrocardiogram as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the explainable classification method for cardio-cerebral health monitoring based on a multi-stage electrocardiogram as described in any one of claims 1 to 6 is implemented.

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