Heart risk assessment method and apparatus, computer device and storage medium

Abstract

A heart risk assessment method and apparatus, a computer device and a storage medium. The method comprises: acquiring electrocardiographic data corresponding to target electrocardiographic measurement matched with a clinical indication, the clinical indication being used for indicating whether exercise stress electrocardiographic measurement can be carried, and the target electrocardiographic measurement at least comprising resting electrocardiographic measurement (S102); according to the target electrocardiographic measurement, analyzing a high-frequency component in a QRS complex in the electrocardiographic data, so as to obtain a corresponding reference feature (S104); and according to the reference feature, determining a heart risk assessment level and a heart risk type, the heart risk assessment level serving as a reference index for triage of a subject, and the heart risk type serving as a reference index for assessment of the risk type possibly presenting in the heart of the subject (S106). Using the present method can improve the accuracy of heart health condition recognition in a non-invasive mode.

Description

Cardiac risk assessment method, device, computer equipment and storage medium

[0001] This application claims priority to a Chinese patent application filed with the Patent Office of China on July 24, 2023, with application number 202310907009X and application name “Cardiac risk assessment method, device, computer equipment and storage medium”, all contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to a cardiac risk assessment method, apparatus, computer equipment and storage medium. Background Art

[0003] Currently, cardiac activity-related information is usually analyzed based on the ST-T segment data representing the cardiac repolarization phase in the electrocardiogram (ECG) to achieve cardiac risk assessment. However, many potential cardiac problems do not show any abnormalities in the ST-T segment data, resulting in low accuracy in cardiac risk assessment. If a more accurate assessment of cardiac risk is required to more effectively identify cardiac health status, it must be achieved through invasive methods such as coronary angiography and myocardial biopsy. However, invasive assessment methods will have more or less impact on the physical health of the subject. Summary of the Invention

[0004] According to various embodiments disclosed in the present application, a cardiac risk assessment method, apparatus, computer device, and storage medium are provided.

[0005] A cardiac risk assessment method, comprising:

[0006] Acquiring ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a stress exercise ECG test can be performed; the target ECG test includes at least a resting ECG test;

[0007] Analyzing the high-frequency components of the QRS complex in the electrocardiogram data according to the target electrocardiogram detection to obtain corresponding reference features;

[0008] The cardiac risk assessment level and cardiac risk type are determined based on the reference characteristics; the cardiac risk assessment level serves as a reference indicator for triaging the subject; and the cardiac risk type serves as a reference indicator for assessing the possible risk type of the subject's heart.

[0009] In one embodiment, if the target ECG detection includes only a resting ECG detection, the ECG data includes the subject's age and resting ECG signal, and the reference feature includes a first resting reference feature; and analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain the corresponding reference feature includes:

[0010] Analyzing the high-frequency components of the QRS complex in the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve;

[0011] A first resting reference feature is determined based on the high-frequency QRS envelope curve and the age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold.

[0012] In one embodiment, the reference feature further includes a second resting reference feature; and the analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain the corresponding reference feature further includes:

[0013] determining a second resting reference feature according to the high-frequency QRS envelope curve and the age; the second resting reference feature comprising the number of resting positive leads, the number of resting critical leads, and the first target lead number;

[0014] Determining the cardiac risk assessment level and the cardiac risk type according to the reference features includes:

[0015] determining a first risk assessment level according to the first resting reference feature;

[0016] determining a second risk assessment level according to the second resting reference feature;

[0017] A cardiac risk assessment level and a cardiac risk type are determined according to the first risk assessment level and the second risk assessment level.

[0018] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes the target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead.

[0019] In one embodiment, if the target ECG test also includes a stress exercise ECG test, the ECG data includes the subject's age, resting ECG signal, and exercise ECG signal, and the reference features include a first resting reference feature and a first exercise reference feature; and analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG test to obtain the corresponding reference features includes:

[0020] Analyzing the high-frequency components of the QRS complex in the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve;

[0021] determining a first resting reference feature based on the high-frequency QRS envelope curve and the age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number, and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to a first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to a first voltage threshold;

[0022] Analyzing the high-frequency components of the QRS complex in the exercise electrocardiogram signal to obtain a high-frequency QRS waveform curve;

[0023] determining the maximum heart rate of the subject according to the exercise electrocardiogram signal;

[0024] A first motion reference feature is determined based on the high-frequency QRS waveform curve, the age and the maximum heart rate; the first motion reference feature includes the number of motion-positive leads, the number of motion-critical leads, the number of third target leads, the number of fourth target leads and the number of fifth target leads; the third target lead number refers to the number of motion leads whose corresponding first amplitude decrease relative value is greater than or equal to the first relative value threshold; the fourth target lead number refers to the number of motion leads whose corresponding high-frequency QRS waveform curve shows a downward and upward repeated fluctuation trend within the first time period; the fifth target lead number refers to the number of motion leads whose corresponding second amplitude decrease relative value is greater than or equal to the second relative value threshold.

[0025] In one embodiment, the reference features further include a second resting reference feature and a second motion reference feature; and the analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain the corresponding reference features further includes:

[0026] determining a second resting reference feature according to the high-frequency QRS envelope curve and the age; the second resting reference feature comprising the number of resting positive leads, the number of resting critical leads, and the first target lead number;

[0027] Determining a second motion reference feature based on the high-frequency QRS waveform curve, the age, and the maximum heart rate; the second motion reference feature includes the number of motion-positive leads and the number of motion-critical leads;

[0028] Determining the cardiac risk assessment level and the cardiac risk type according to the reference features includes:

[0029] determining a first risk assessment level according to the first resting reference feature;

[0030] determining a second risk assessment level according to the second rest reference feature and the second motion reference feature;

[0031] determining a third risk assessment level based on the first motion reference feature;

[0032] A cardiac risk assessment level and a cardiac risk type are determined according to the first risk assessment level, the second risk assessment level, and the third risk assessment level.

[0033] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes the target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead; the first motion reference feature also includes a first amplitude decrease relative value and a second amplitude decrease relative value corresponding to each motion lead; the second motion reference feature also includes a second amplitude decrease relative value corresponding to each motion lead.

[0034] In one embodiment, the method further comprises:

[0035] determining corresponding risk assessment features according to the cardiac risk type based on the electrocardiogram data;

[0036] A concern level of the corresponding cardiac risk type is determined based on the risk assessment characteristics.

[0037] A cardiac risk assessment device, comprising:

[0038] An acquisition module, configured to acquire ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a stress exercise ECG test can be performed; the target ECG test includes at least a resting ECG test;

[0039] a feature determination module, configured to analyze the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain corresponding reference features;

[0040] A risk assessment module is used to determine a cardiac risk assessment level and a cardiac risk type based on the reference characteristics; the cardiac risk assessment level serves as a reference indicator for triaging the subject; and the cardiac risk type serves as a reference indicator for assessing the possible risk type of the subject's heart.

[0041] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps in various method embodiments when executing the computer-readable instructions.

[0042] A computer-readable storage medium stores computer-readable instructions, which implement the steps of various method embodiments when executed by a processor.

[0043] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] FIG1 is a schematic flow chart of a cardiac risk assessment method according to one embodiment;

[0046] FIG2 is a schematic diagram of a high-frequency QRS waveform curve in one embodiment;

[0047] FIG3 is a schematic diagram of a high-frequency QRS envelope curve in one embodiment;

[0048] FIG4 is a schematic flow chart of a cardiac risk assessment method according to another embodiment;

[0049] FIG5 is a block diagram of a cardiac risk assessment device according to one embodiment;

[0050] FIG6 is a block diagram of a computer device according to one embodiment. DETAILED DESCRIPTION

[0051] In order to make the technical solutions and advantages of this application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] The cardiac risk assessment method provided in this application can be applied to a terminal, a server, or an interactive system including a terminal and a server, and implemented through the interaction between the terminal and the server, without specific limitations herein. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, electrocardiogram monitoring devices, and portable wearable devices. The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0053] In one embodiment, as shown in FIG1 , a cardiac risk assessment method is provided. The method is described using an example of application to a server, and specifically includes the following steps:

[0054] S102, obtaining ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a stress exercise ECG test can be performed; the target ECG test includes at least a resting ECG test.

[0055] The clinical indication is used to indicate the ECG test applicable to the corresponding subject, specifically to indicate whether the corresponding subject can undergo stress exercise ECG testing, specifically including clinical data such as whether the subject is in the acute stage of hypoglycemia, hypotension and myocardial infarction, and whether the vital signs are stable. If the vital signs are stable and hypoglycemia, hypotension and myocardial infarction are excluded, indicating that the corresponding subject can undergo stress exercise ECG testing, then the target ECG test that matches the clinical indication includes resting ECG testing and stress exercise ECG testing. Otherwise, indicating that the corresponding subject cannot undergo stress exercise ECG testing, then the target ECG test that matches the clinical indication includes resting ECG testing. The subject is in a resting state during the resting ECG test. The subject is in an exercise state during the stress exercise ECG test to increase the subject's cardiac load through exercise. The target ECG test refers to the type of ECG test applicable to the subject. It can be understood that the specific content of the above clinical indications is only an example and is not used for specific limitation.

[0056] Specifically, the subject's ECG data is obtained, which is the ECG data collected during the target ECG detection process. The target ECG detection matches the subject's clinical indication, that is, the target ECG detection is determined by the subject's clinical indication.

[0057] In one embodiment, if the target ECG test includes a resting ECG test, the acquired ECG data includes the subject's age and the resting ECG signal collected during the resting ECG test. If the target ECG test includes a resting ECG test and a stress exercise ECG test, the acquired ECG data includes the subject's age, the resting ECG signal collected during the resting ECG test, and the exercise ECG signal collected during the stress exercise ECG test.

[0058] S104 , analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain corresponding reference features.

[0059] Specifically, ECG data includes multiple QRS complexes that reflect changes in left and right ventricular depolarization potential and timing. Each QRS complex consists of high-frequency and low-frequency components. Each target ECG test has a corresponding reference feature. By analyzing the high-frequency components of the QRS complex in the ECG data corresponding to each target ECG test, the reference feature corresponding to that target ECG test can be determined.

[0060] In one embodiment, if the target ECG detection includes a resting ECG detection, the high-frequency components of the QRS complex in the resting ECG signal are analyzed to obtain corresponding reference features. If the target ECG detection includes a resting ECG detection and a load exercise ECG detection, the high-frequency components of the QRS complex in the resting ECG signal are analyzed to obtain corresponding reference features, and the high-frequency components of the QRS complex in the exercise ECG signal are analyzed to obtain corresponding reference features. Among them, the reference features determined based on the resting ECG signal can be understood as resting reference features, and the resting reference features at least include a first resting reference feature and may also include a second resting reference feature. The reference features determined based on the exercise ECG signal can be understood as motion reference features, and the motion reference features at least include a first motion reference feature and may also include a second motion reference feature.

[0061] S106, determining a cardiac risk assessment level and a cardiac risk type based on the reference characteristics; the cardiac risk assessment level serves as a reference indicator for triaging the subject; the cardiac risk type serves as a reference indicator for assessing the possible risk type of the subject's heart.

[0062] The cardiac risk assessment level is used to characterize the risk of heart problems. For example, the higher the cardiac risk assessment level, the greater the risk of heart problems, so that doctors can guide and triage the subject based on the subject's clinical symptoms. The cardiac risk type is used to characterize the possible risk type of the subject's heart, that is, it is used to characterize the type of heart problems that the subject may have, specifically including myocardial injury risk, sudden cardiac death risk, coronary artery stenosis risk and at least one of the others. The myocardial injury risk characterizes the possibility of myocardial injury, the sudden cardiac death risk characterizes the possibility of sudden cardiac death, the coronary artery stenosis risk characterizes the possibility of coronary artery stenosis, and the others characterize the possibility of no risk of any of myocardial injury, sudden cardiac death and coronary artery stenosis. It is understandable that doctors can also triage subjects based on the cardiac risk assessment level, cardiac risk type and clinical symptoms.

[0063] Specifically, the obtained reference features are evaluated and analyzed to obtain a corresponding cardiac risk assessment score, and the corresponding cardiac risk assessment level and cardiac risk type are determined based on the cardiac risk assessment score for the doctor's reference, so that the doctor can accurately understand the risk of the subject's heart problems and the possible risk types, thereby accurately identifying the subject's heart health status. If the target ECG test includes a resting ECG test, the cardiac risk assessment score is determined based on the resting reference index. If the target ECG test includes a resting ECG test and a stress exercise ECG test, the cardiac risk assessment score is determined based on the resting reference index and the stress exercise reference index, thereby determining the subject's cardiac risk assessment level and cardiac risk type.

[0064] In one embodiment, reference features obtained according to target ECG detection are input into a corresponding preset risk assessment function or a pre-trained risk assessment model to obtain a corresponding cardiac risk assessment score. It will be appreciated that when the reference features are input into a pre-trained risk assessment model, the risk assessment model may directly output a corresponding cardiac risk assessment level and may also simultaneously output a corresponding cardiac risk type, as determined by the training dataset (including input features and corresponding output features) used to train the risk assessment model.

[0065] In one embodiment, the cardiac risk assessment score is compared with various preset score intervals to determine a corresponding cardiac risk assessment level, and thus a cardiac risk type. For example, in a scenario where the cardiac risk assessment levels include six levels, from Level 1 to Level 6, each of which is pre-configured with a corresponding preset score interval, namely [0, 10], [11, 30], [31, 50], [51, 60], [61, 70], and [71, 100], if the cardiac risk assessment score is between [0, 10], the cardiac risk assessment level is determined to be Level 1, and the cardiac risk type is determined to be "other."

[0066] In one embodiment, if the cardiac risk assessment level and cardiac risk type are output for reference by the referral physician, the subject's triage may include recommending the subject to an inpatient department, recommending the subject to an outpatient department, or recommending the subject to go home (no need for medical treatment). It is understood that if the subject is recommended to an outpatient department or inpatient department, the subject may also be triaged to the corresponding department or physician based on cardiac risk type. The referral physician may include a physician at a physical examination institution or department, or a physician assigned to a cardiovascular department within a hospital to guide and triage subjects preparing to visit the department, without specific limitation. For example, using the six-level cardiac risk assessment scale, if the cardiac risk assessment level output for the subject is level 1, the referral physician may recommend the subject to go home. If the cardiac risk assessment level is level 2 or 3, the referral physician may recommend the subject to an outpatient department. Otherwise, the referral physician may recommend the subject to an inpatient department. It is worth mentioning that the guiding doctor also needs to guide and divert the subjects based on their clinical symptoms.

[0067] In one embodiment, if the output cardiac risk assessment level and cardiac risk type are for reference by outpatient doctors, the subjects will be triaged including recommending hospitalization for the subjects, recommending regular checkups for the subjects, and recommending that the subjects seek medical treatment again if they experience any discomfort. For example, taking the cardiac risk assessment level as an example, if the cardiac risk assessment level output for the subject is the first level, the outpatient doctor may recommend that the subject seek medical treatment again if they experience any discomfort. If the cardiac risk assessment level is the second or third level, the subject may be recommended to have regular checkups. Otherwise, the subject may be recommended to be hospitalized. It is worth noting that outpatient doctors are also required to provide further diagnosis and treatment and / or testing reference suggestions based on the clinical symptoms of the subjects.

[0068] The above-mentioned cardiac risk assessment method obtains ECG data corresponding to a target ECG test that matches the clinical indication, analyzes the high-frequency components of the QRS complex in the corresponding ECG data according to the target ECG test to obtain corresponding reference features, and evaluates and analyzes the reference features to efficiently and accurately obtain a cardiac risk assessment level that characterizes the risk of a subject's heart problems, as well as a cardiac risk type that characterizes the possible risk type of the heart. As a result, the subject's risk of heart problems and risk type can be assessed efficiently and accurately in a non-invasive manner for the doctor's reference, so that the doctor can efficiently and accurately identify the subject's heart health status based on clinical symptoms and provide further diagnosis and / or testing reference recommendations to guide and divert the subject. In addition, the ECG data used to assess the subject's heart risk is collected during a target ECG test that matches the subject's clinical indication, so that while ensuring the subject's safety during the test, the subject's heart risk can be accurately assessed as comprehensively as possible, thereby further improving the accuracy of cardiac risk assessment and further improving the accuracy of cardiac health status identification.

[0069] In one embodiment, if the target ECG detection only includes resting ECG detection, the ECG data includes the subject's age and resting ECG signal, and the reference feature includes a first resting reference feature; S104 includes: analyzing the high-frequency components of the QRS complex in the resting ECG signal to obtain a high-frequency QRS envelope curve; determining the first resting reference feature based on the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold.

[0070] The number of resting positive leads refers to the number of resting leads where the corresponding lead-positive indicator indicates a positive result. This can be used to assess the risk of myocardial ischemia at rest, and the two are positively correlated. The number of resting critical leads refers to the number of resting leads where the corresponding lead-positive indicator indicates a critical result. This can be used to assess the critical risk of myocardial ischemia at rest. Combining this reference feature allows for more accurate identification of myocardial ischemia. A positive lead-positive indicator indicates a positive result when the high-frequency morphology index of the corresponding resting lead is greater than or equal to the second index threshold and the subject's age is greater than or equal to a preset age, or when the high-frequency morphology index of the corresponding resting lead is greater than or equal to the third index threshold and the subject's age is less than a preset age. A critical lead-positive indicator indicates a critical result when the high-frequency morphology index of the corresponding resting lead is greater than the second index threshold and less than the third index threshold and the subject's age is less than a preset age. The first, second, and third index thresholds can be customized, for example, the first index threshold is 20%, the second index threshold is 8%, and the third index threshold is 15%. The first voltage threshold can be customized according to actual conditions, such as 4uV (microvolts). The preset age can also be customized, such as 50 years old.

[0071] Specifically, each QRS complex in the resting ECG signal is sequentially aligned, averaged, and high-frequency filtered to obtain high-frequency QRS complex data (high-frequency band data of the QRS complex). Alternatively, the QRS complex in the resting ECG signal is sequentially high-frequency filtered, aligned, and averaged to obtain high-frequency QRS complex data. Alternatively, the high-frequency ECG signal is extracted from the resting ECG signal by analyzing the resting ECG signal, and the QRS complexes in the high-frequency ECG signal are then sequentially aligned and averaged to obtain high-frequency QRS complex data. The high-frequency QRS envelope curve can be formed based on the high-frequency QRS complex data. Thus, performing the above data processing on the resting ECG signal can obtain the high-frequency QRS envelope curve. The high-frequency QRS envelope curve corresponding to each resting lead is analyzed to obtain the total area of ​​each amplitude reduction region on the high-frequency QRS envelope curve as a first total area, and the total area below the high-frequency QRS envelope curve as a second total area. The ratio of the first total area to the second total area is used as the high-frequency morphology index corresponding to the resting lead. Based on the subject's age and the high-frequency morphology index corresponding to each resting lead, the corresponding lead positivity index is determined. Resting leads with positive indicators are screened and counted to obtain the number of resting-positive leads. Resting leads with critical indicators are screened and counted to obtain the number of critical resting leads. Resting leads with high-frequency morphology indices greater than or equal to a first index threshold are screened and counted to obtain the first target number of leads. The root mean square (RMS) value of the high-frequency QRS envelope curve (i.e., high-frequency QRS complex data) corresponding to each resting lead is calculated to obtain the RMS voltage of the corresponding resting lead. Resting leads with RMS voltages less than or equal to the first voltage threshold are screened and counted to obtain the second target number of leads.

[0072] In one embodiment, if the reference feature includes a first resting reference feature, the cardiac risk assessment score includes a first risk assessment score. The first resting reference feature determined based on the subject's age and resting ECG signal is input into a first risk assessment function preconfigured for resting ECG detection or a pre-trained first risk assessment model to obtain a corresponding first risk assessment score, the first risk assessment score is compared with each preset score interval to obtain a first risk assessment level, the first risk type is determined based on the first risk assessment level, the first risk assessment level is determined as a cardiac risk assessment level, and the first risk type is determined as a cardiac risk type. For example, if the first risk assessment level is the first level, the first risk type is other, otherwise, the first risk type is myocarditis risk.

[0073] In one embodiment, the expression of the first risk assessment function preconfigured for resting ECG detection is as follows:

[0074]

[0075] in, is the first risk assessment score, is the number of resting critical leads, is the number of resting positive leads, is the first target lead number, is the second target lead number.

[0076] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage, wherein the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead, and the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead. In this embodiment, the expression of the first risk assessment function preconfigured for resting ECG detection can also be expressed as follows:

[0077]

[0078] in, is an additional score determined based on the target high-frequency morphology index, is the additional fraction determined based on the target RMS voltage, and is obtained based on the following expressions:

[0079]

[0080]

[0081] in, is the target high-frequency morphological index, is the target RMS voltage in uV (microvolts).

[0082] In the above embodiment, if the clinical indication indicates that the subject cannot undergo stress exercise ECG testing, the first resting reference feature is determined based on the resting ECG signal collected during the resting ECG testing process and the subject's age, so as to accurately assess the cardiac risk assessment level and cardiac risk type based on the first resting reference feature.

[0083] In one embodiment, the reference feature also includes a second resting reference feature; S104 also includes: determining the second resting reference feature based on the high-frequency QRS envelope curve and age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the number of first target leads; S106 includes: determining a first risk assessment level based on the first resting reference feature; determining a second risk assessment level based on the second resting reference feature; determining a cardiac risk assessment level and a cardiac risk type based on the first risk assessment level and the second risk assessment level.

[0084] The second resting reference signature is a subset of the first resting reference signature. Thus, when determining the first resting reference signature, the second resting reference signature can also be determined simultaneously. The first resting reference signature is used to assess the likelihood of myocarditis, while the second resting reference signature is used to assess the likelihood of sudden cardiac death.

[0085] In one embodiment, if the reference feature includes a first resting reference feature and a second resting reference feature, the cardiac risk assessment score includes a first risk assessment score and a second risk assessment score. The first resting reference feature is input into a first risk assessment function preconfigured for resting ECG detection or a pretrained first risk assessment model to obtain a corresponding first risk assessment score, and the first risk assessment score is compared with each preset score interval to obtain a first risk assessment level. The second resting reference feature is input into a second risk assessment function preconfigured for resting ECG detection or a second risk assessment model to obtain a corresponding second risk assessment score, and the second risk assessment score is compared with each preset score interval to obtain a second risk assessment level. The maximum value (highest level) between the first risk assessment level and the second risk assessment level is determined as the cardiac risk assessment level. A first risk type is determined based on the first risk assessment level, a second risk type is determined based on the second risk assessment level, and a cardiac risk type is determined based on the first risk type and the second risk type.

[0086] For example, if the first risk assessment level is level 2 and the second risk assessment level is level 1, the cardiac risk assessment level is determined to be level 2, the first risk type is determined to be myocarditis risk, the second risk type is determined to be other, and thus the cardiac risk type is determined to be myocarditis risk. If the first risk assessment level is level 3 and the second risk assessment level is level 2, the cardiac risk assessment level is determined to be level 3, the first risk type is determined to be myocarditis risk, the second risk type is determined to be sudden cardiac death risk, and thus the cardiac risk types are determined to be myocarditis risk and sudden cardiac death risk.

[0087] In one embodiment, the expression of the second risk assessment function preconfigured for resting ECG detection is as follows:

[0088]

[0089] in, is a second risk assessment score determined based on the second resting reference feature, is the number of resting critical leads, is the number of resting positive leads, is the first target lead number.

[0090] In the above embodiment, if the clinical indication indicates that the subject cannot undergo stress exercise ECG testing, the first resting reference feature and the second resting reference feature are determined based on the resting ECG signal and the subject's age, so as to more accurately evaluate the cardiac risk assessment level and cardiac risk type in combination with the first resting reference feature and the second resting reference feature.

[0091] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes a target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead.

[0092] In one embodiment, the expression of the second risk assessment function preconfigured for resting ECG detection can also be expressed as follows:

[0093]

[0094] in, is the additional score determined based on the target high-frequency morphology index and is obtained based on the following expression:

[0095]

[0096] in, is the target high-frequency morphological index.

[0097] In the above embodiment, based on the first resting reference feature and the second resting reference feature including the additional reference feature, it is possible to more accurately identify and distinguish the possible risk types of the heart, and more accurately assess the risk of heart problems, so that doctors can more accurately identify the heart health status of the subjects in combination with clinical symptoms, and thus provide reference suggestions for further diagnosis, treatment, testing, etc., to achieve more accurate guidance and diversion.

[0098] In one embodiment, if the target ECG detection also includes load exercise ECG detection, the ECG data includes the age, resting ECG signal and exercise ECG signal of the subject, and the reference features include a first resting reference feature and a first exercise reference feature; S104 includes: analyzing the high-frequency components of the QRS wave group in the resting ECG signal to obtain a high-frequency QRS envelope curve; determining the first resting reference feature according to the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold; The high-frequency components of the QRS wave group in the exercise ECG signal are analyzed to obtain a high-frequency QRS waveform curve; the maximum heart rate of the subject is determined based on the exercise ECG signal; the first motion reference feature is determined based on the high-frequency QRS waveform curve, age and maximum heart rate; the first motion reference feature includes the number of motion-positive leads, the number of motion-critical leads, the number of third target leads, the number of fourth target leads and the number of fifth target leads; the number of third target leads refers to the number of motion leads whose corresponding first amplitude decrease relative value is greater than or equal to the first relative value threshold; the number of fourth target leads refers to the number of motion leads whose corresponding high-frequency QRS waveform curve shows a trend of repeated decrease and increase within a first time period; the number of fifth target leads refers to the number of motion leads whose corresponding second amplitude decrease relative value is greater than or equal to the second relative value threshold.

[0099] Exercise ECG signals are ECG signals collected during the stress exercise ECG test process. The stress exercise ECG test process includes multiple stages, which can specifically include three stages: resting stage, exercise stage and recovery stage. Exercise ECG signals include ECG signals of each stage. The division of stages is not limited to this and can be divided according to actual conditions. Maximum heart rate refers to the maximum heart rate of the subject during the entire stress exercise ECG test process. The number of exercise-positive leads refers to the number of exercise leads where the corresponding lead positive indicator indicates positive, which can be used to assess the risk of myocardial ischemia under stress exercise conditions, and the two are positively correlated. The number of critical exercise leads refers to the number of exercise leads where the corresponding lead positive indicator indicates critical, which can be used to assess the critical risk of myocardial ischemia under stress exercise conditions. Combining this reference feature can more accurately assess the myocardial ischemia under stress exercise conditions.

[0100] The lead positive indicator indicates positive, which is used to characterize that the first amplitude decrease relative value of the corresponding motion lead is greater than the third relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is less than the preset age, and the maximum heart rate of the subject is greater than 80% of the target heart rate, or, it is used to characterize that the first amplitude decrease relative value of the corresponding motion lead is greater than the fourth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is less than the preset age, and the maximum heart rate of the subject is less than or equal to 80% of the target heart rate, or, it is used to characterize that the first amplitude decrease relative value of the corresponding motion lead is greater than the fourth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is greater than or equal to the preset age, and the maximum heart rate of the subject is greater than 80% of the target heart rate, or, it is used to characterize that the first amplitude decrease relative value of the corresponding motion lead is greater than the fifth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is greater than or equal to the preset age, and the maximum heart rate of the subject is less than or equal to 80% of the target heart rate. The first relative threshold, second relative threshold, third relative threshold, fourth relative threshold, fifth relative threshold, preset absolute threshold, and preset age are customized based on actual conditions, for example, 55%, 40%, 60%, 50%, 40%, 1uV (microvolt), and 50 years old, respectively. The target heart rate is determined based on the subject's age, for example, target heart rate = (220 - subject's age) × 85%.

[0101] The lead positive indicator is critical, which is used to indicate that the relative value of the first amplitude decrease of the corresponding motion lead is greater than 90% of the third relative value threshold and less than or equal to the third relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is less than the preset age, and the maximum heart rate of the subject is greater than 80% of the target heart rate, or the relative value of the first amplitude decrease of the corresponding motion lead is greater than 90% of the fourth relative value threshold and less than or equal to the fourth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is less than the preset age, and the maximum heart rate of the subject is less than or equal to 80% of the target heart rate. %, or, the first amplitude decrease relative value used to characterize the corresponding motion lead is greater than 90% of the fourth relative value threshold and less than or equal to the fourth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is greater than or equal to the preset age, and the maximum heart rate of the subject is greater than 80% of the target heart rate, or, the first amplitude decrease relative value used to characterize the corresponding motion lead is greater than 90% of the fifth relative value threshold and less than or equal to the fifth relative value threshold, the absolute value of the amplitude decrease is greater than the preset absolute value threshold, the age of the subject is greater than or equal to the preset age, and the maximum heart rate of the subject is less than or equal to 80% of the target heart rate.

[0102] The first time period includes a period of time before exercise, during exercise, and after exercise. The period of time before exercise is in the resting stage, the period of time during exercise includes the entire exercise stage, and the period of time after exercise is in the recovery stage. The period of time before exercise, during exercise, and after exercise are continuous time periods. The second time period includes a period of time before exercise and a period of time during exercise, or the second time period includes a period of time during exercise. The period of time before exercise and the period of time during exercise are continuous time periods. The period of time during exercise can be customized according to actual conditions, such as the first 3 minutes of exercise. The first time period may specifically include the second time period, which means that the second time period is a subinterval of the first time period. The second relative value of amplitude decrease is used to characterize the descending slope or steepness of the high-frequency QRS waveform curve in the second time period. If the second relative value of amplitude decrease exceeds the second relative value threshold, it indicates that the descending slope or steepness is large enough, which indicates the possibility of coronary artery stenosis. The first relative value of amplitude decrease can be understood as the relative value of the amplitude decrease of the high-frequency QRS waveform curve in the first time period, and the second relative value of amplitude decrease can be understood as the relative value of the amplitude decrease of the high-frequency QRS waveform curve in the second time period.

[0103] Specifically, according to the first resting reference feature determination method disclosed in one or more embodiments of the present application, the corresponding first resting reference feature is determined according to the resting ECG signal and the age of the subject, which will not be repeated here. The exercise ECG signal includes the QRS wave group corresponding to each heartbeat of the subject during the entire load exercise ECG detection process. The exercise ECG signal is divided into multiple ECG signal subsets according to the time sequence and the preset moving step size through a window function, and each ECG signal subset includes QRS wave groups corresponding to multiple heartbeats. For each ECG signal subset, the QRS wave groups corresponding to the multiple heartbeats included therein are aligned, averaged and high-frequency filtered in sequence to obtain corresponding high-frequency QRS wave group data (high-frequency band data of the QRS wave group), and the root mean square of the high-frequency QRS wave group data is calculated to obtain the corresponding root mean square voltage, which is used as the root mean square voltage / intensity / amplitude corresponding to the ECG signal subset. The root mean square voltage / intensity / amplitude corresponding to each subset of ECG signals is smoothed according to the time sequence to obtain the high-frequency QRS waveform curve corresponding to the exercise ECG signal. Therefore, the high-frequency QRS waveform curve can be understood as a high-frequency QRS time-intensity curve. Among them, the window length and the preset moving step of the window function can be customized according to actual needs. For example, the window length is set to 10 seconds, and the preset moving step is set to 10 seconds or one heartbeat cycle. One heartbeat cycle refers to the time interval between two adjacent heartbeats, which is not specifically limited here. According to the time sequence, it refers to the order of detection time according to the acquisition time of the signal / load exercise ECG detection process. According to the method of extracting the heart rate sequence from the ECG signal disclosed in the prior art, the heart rate sequence of the subject is extracted from the exercise ECG data, and the maximum value from the heart rate sequence is selected as the maximum heart rate of the subject.

[0104] Furthermore, a point with the maximum RMS voltage within the first time period is selected from the high-frequency QRS waveform curve as a first reference point, and a point with a later time than the first reference point and the minimum RMS voltage is selected from the high-frequency QRS waveform curve within the first time period as a second reference point. The RMS voltage of the first reference point is subtracted from the RMS voltage of the second reference point to obtain a first absolute value of amplitude decrease, and the ratio of the first absolute value of amplitude decrease to the RMS voltage of the first reference point is determined as a first relative value of amplitude decrease. Motion leads with corresponding first relative value of amplitude decrease greater than or equal to the first relative value threshold are screened and counted to obtain a third target number of leads. A lead-positive index for the corresponding high-frequency QRS waveform curve is determined based on the first relative value of amplitude decrease, the first absolute value of amplitude decrease, the subject's age, and the maximum heart rate, as the lead-positive index corresponding to the corresponding motion lead. Motion leads with positive indications from the lead-positive index are screened and counted to obtain the number of motion-positive leads, and motion leads with critical indications from the lead-positive index are screened and counted to obtain the number of motion-critical leads. The changing trend of the high-frequency QRS waveform curve corresponding to each motion lead in the first time period is analyzed to screen and count the motion leads whose corresponding high-frequency QRS waveform curve shows a trend of repeated fluctuation of decreasing and increasing in the first time period, and obtain the fourth target number of leads.

[0105] The point with the lowest RMS voltage on the high-frequency QRS waveform curve within the second time period is selected as the third reference point. The point with the highest RMS voltage that is earlier than the third reference point on the high-frequency QRS waveform curve within the second time period is selected as the fourth reference point. The RMS voltage at the fourth reference point is subtracted from the RMS voltage at the third reference point to obtain the absolute value of the second amplitude decrease within the second time period. The ratio of the absolute value of the second amplitude decrease to the RMS voltage at the fourth reference point is used as the relative value of the second amplitude decrease. The number of motor leads whose relative values ​​of the second amplitude decrease are greater than or equal to the second relative value threshold is selected and counted to obtain the fifth target number of leads.

[0106] In one embodiment, the high-frequency QRS waveform exhibits a trend of repeated downward and upward fluctuations during the first time period, which means that the high-frequency QRS curve exhibits a downward trend a total of two or more times during the first time period, and the upward and downward trends alternate. For example, the high-frequency QRS waveform exhibits a "W" or "inverted N" waveform during the first time period.

[0107] In one embodiment, if the reference feature includes a first resting reference feature and a first motion reference feature, the cardiac risk assessment score includes a first risk assessment score and a third risk assessment score. According to one or more embodiments of the present application, the corresponding first risk assessment level and the first risk type are determined based on the first resting reference feature. The first motion reference feature is input into a risk assessment function preconfigured for stress exercise ECG detection or a pre-trained risk assessment model to obtain a corresponding third risk assessment score, the third risk assessment score is compared with each preset score interval to obtain a third risk assessment level, and the third risk type is determined based on the third risk assessment level. The highest level of the first risk assessment level and the third risk assessment level is determined as the cardiac risk assessment level, and the cardiac risk type is determined based on the first risk type and the third risk type. For example, if the first risk assessment level is the first level and the third risk assessment level is the fifth level, the first risk type is other and the third risk type is coronary artery stenosis risk, the cardiac risk assessment level is determined to be the fifth level, and the cardiac risk type is determined to be coronary artery stenosis risk.

[0108] In one embodiment, the expression of the risk assessment function preconfigured for stress exercise ECG detection is as follows:

[0109]

[0110] in, is the third risk assessment score, is the number of motion critical leads, is the number of motor-positive leads, is the third target lead number, is the fourth target lead number, is the fifth target lead number.

[0111] In one embodiment, the first motion reference feature further includes a first amplitude decrease relative value and a second amplitude decrease relative value corresponding to each motion lead. In this embodiment, the expression of the risk assessment function preconfigured for stress exercise ECG detection can also be expressed as follows:

[0112]

[0113] in, is an additional score determined based on the relative value of the first amplitude decrease corresponding to each motion lead, is an additional score determined based on the relative value of the second amplitude decrease corresponding to each motion lead, and is obtained based on the following expressions:

[0114]

[0115]

[0116] In the above embodiment, if the clinical indication indicates that the subject is capable of undergoing stress exercise ECG testing, a first resting reference feature is determined based on the resting ECG signal collected during the resting ECG testing process and the subject's age, and a first motion reference feature is determined based on the motion ECG signal collected during the stress exercise ECG testing process and the subject's age, so as to accurately assess the cardiac risk assessment level and cardiac risk type based on the first resting reference feature and the first motion reference feature.

[0117] In one embodiment, the reference feature also includes a second resting reference feature and a second motion reference feature; S104 also includes: determining the second resting reference feature based on the high-frequency QRS envelope curve and age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the number of first target leads; determining the second motion reference feature based on the high-frequency QRS waveform curve, age and maximum heart rate; the second motion reference feature includes the number of motion-positive leads and the number of motion-critical leads; S106 includes: determining a first risk assessment level based on the first resting reference feature; determining a second risk assessment level based on the second resting reference feature and the second motion reference feature; determining a third risk assessment level based on the first motion reference feature; determining a cardiac risk assessment level and a cardiac risk type based on the first risk assessment level, the second risk assessment level and the third risk assessment level.

[0118] The second resting reference signature is a subset of the first resting reference signature. Thus, when determining the first resting reference signature, the second resting reference signature can also be determined simultaneously. Similarly, the second motion reference signature is a subset of the first motion reference signature. Thus, when determining the first motion reference signature, the second motion reference signature can also be determined simultaneously. The first resting reference signature is used to assess the likelihood of myocarditis, while the second resting reference signature and the second motion reference signature are used together to assess the likelihood of sudden cardiac death. The first motion reference signature is used to assess the likelihood of coronary artery stenosis.

[0119] In one embodiment, if the reference features include a first resting reference feature, a second resting reference feature, a first motion reference feature, and a second motion reference feature, the cardiac risk assessment score includes a first risk assessment score, a second risk assessment score, and a third risk assessment score. According to one or more embodiments of the present application, a first risk assessment score is determined based on the first resting reference feature, thereby determining a first risk assessment level and a first risk type, and a third risk assessment score is determined based on the first motion reference feature, thereby determining a third risk assessment level and a third risk type. The second resting reference feature and the second motion reference feature are input into a risk assessment function or risk assessment model configured for a combined resting ECG test and a stress exercise ECG test to obtain a second risk assessment score, the second risk assessment score is compared with each preset score interval to obtain a second risk assessment level, and a second risk type is determined based on the second risk assessment level. Furthermore, the highest level among the first risk assessment level, the second risk assessment level, and the third risk assessment level is determined as the cardiac risk assessment level, and the cardiac risk type is determined based on the first risk type, the second risk type, and the third risk type.

[0120] For example, if the first risk assessment level is the second level, the second risk assessment level is the fourth level, and the third risk assessment level is the third level, then the first risk type is myocarditis risk, the second risk type is sudden cardiac death risk, and the third risk type is coronary artery stenosis risk, then the cardiac risk assessment level is determined to be the fourth level, and the cardiac risk types are determined to be myocarditis risk, sudden cardiac death risk, and coronary artery stenosis risk.

[0121] In one embodiment, the risk assessment function for the combined configuration of resting ECG detection and stress exercise ECG detection is expressed as follows:

[0122]

[0123] in, is a second risk assessment score determined jointly based on the second rest reference feature and the second motion reference feature, is the number of resting critical leads, is the number of resting positive leads, is the first target lead number, is the number of motion critical leads, is the number of motor-positive leads.

[0124] In the above embodiment, if the clinical indication indicates that the subject is capable of undergoing stress exercise ECG testing, the first resting reference feature and the second resting reference feature are determined based on the resting ECG signal and the subject's age, and the first motion reference feature and the second motion reference feature are determined based on the exercise ECG signal and the subject's age, so as to more accurately assess the cardiac risk assessment level and cardiac risk type by combining the first resting reference feature, the second resting reference feature, the first motion reference feature and the second motion reference feature.

[0125] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes a target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead; the first motion reference feature also includes a first amplitude decrease relative value and a second amplitude decrease relative value corresponding to each motion lead; the second motion reference feature also includes a second amplitude decrease relative value corresponding to each motion lead.

[0126] In one embodiment, the risk assessment function for the combined configuration of resting ECG detection and stress exercise ECG detection can also be expressed as follows:

[0127]

[0128] in, is an additional score determined based on the target high-frequency morphology index, is an additional score determined based on the relative value of the second amplitude decrease corresponding to each motion lead, and is obtained based on the following expressions:

[0129]

[0130] in, is an additional score determined based on the target high-frequency morphology index, is an additional score determined based on the relative value of the second amplitude decrease corresponding to each motion lead, and is obtained based on the following expressions:

[0131]

[0132]

[0133] in, is the target high-frequency morphological index.

[0134] In the above embodiment, based on the first resting reference feature, the second resting reference feature, the first motion reference feature and the second motion reference feature including the additional reference feature, it is possible to more accurately identify and distinguish the possible risk types of the heart, and more accurately assess the risk of heart problems, so that doctors can more accurately identify the heart health status of the subjects in combination with clinical symptoms, and thus provide reference suggestions for further diagnosis, treatment, testing, etc., to achieve more accurate guidance and diversion.

[0135] In one embodiment, the cardiac risk assessment method further includes: determining corresponding risk assessment features according to the cardiac risk type based on the electrocardiogram data; and determining a concern level of the corresponding cardiac risk type based on the risk assessment features.

[0136] Specifically, the concern level represents different levels of concern and can be used to indicate the likelihood of a subject developing a corresponding cardiac risk type. After determining the cardiac risk type based on the ECG data, corresponding risk assessment features are determined for each determined cardiac risk type based on the ECG data. The corresponding concern level is then determined based on the risk assessment features corresponding to each cardiac risk type for reference by the physician during the diagnosis process. This allows the physician to accurately identify the subject's cardiac health status based on the cardiac risk type, concern level, and clinical symptoms, thereby providing appropriate diagnostic and treatment recommendations. It is understood that at least one of the cardiac risk assessment grade and the risk assessment features corresponding to the cardiac risk type may also be output, allowing the physician to comprehensively consider these reference indicators to identify the cardiac health status. In this embodiment, the output cardiac risk type, concern level, etc. are provided for reference by outpatient or inpatient physicians, allowing them to accurately identify the subject's cardiac health status based on clinical symptoms and provide appropriate diagnostic, treatment, or testing recommendations.

[0137] In one embodiment, the risk assessment features corresponding to each cardiac risk type are input into a pre-trained risk assessment model for that cardiac risk type, which then outputs a corresponding level of concern. Alternatively, the risk assessment features corresponding to each cardiac risk type are input into a pre-configured risk assessment function or pre-trained risk assessment model for that cardiac risk type to obtain a risk assessment score corresponding to that cardiac risk type, thereby determining the corresponding level of concern. Alternatively, each risk assessment feature corresponding to each cardiac risk type is compared with a corresponding preset reference interval to determine a reference level corresponding to each risk assessment feature, and the corresponding level of concern is determined based on the combined reference levels. It will be understood that if the risk assessment features only include features used for quantitative analysis, the corresponding level of concern is determined based on the risk assessment features according to the aforementioned evaluation method. If the risk assessment features include features used for qualitative analysis and features used for quantitative analysis, if the presence of a corresponding cardiac risk type is determined based on the features used for qualitative analysis, the corresponding level of concern can be determined based on the features used for quantitative analysis according to the aforementioned evaluation method. For risk assessment models that use the level of concern as an output feature, both the features used for qualitative analysis and the features used for quantitative analysis can also be input into the corresponding risk assessment model to obtain the corresponding level of concern.

[0138] In one embodiment, the ECG data only includes resting ECG signals. If the cardiac risk type includes myocardial injury risk, the risk assessment feature corresponding to the myocardial injury risk is determined based on the resting ECG signal. If the cardiac risk type also includes sudden cardiac death risk, the risk assessment feature corresponding to the sudden cardiac death risk is also determined based on the resting ECG signal. The risk assessment feature corresponding to the myocardial injury risk includes a first risk assessment feature for assessing the risk of myocarditis and a second risk assessment feature for assessing the risk of heart failure. The first risk assessment feature includes the number of resting positive leads, which is used to determine the type of myocarditis. If the type of myocarditis is acute myocarditis, the first risk assessment feature also includes a target high-frequency morphology index. If the type of myocarditis is chronic myocarditis, the first risk assessment feature also includes a target high-frequency morphology index and an average peak voltage of limb leads. If the type of myocarditis is fulminant myocarditis, the first risk assessment feature also includes QRS duration, target high-frequency morphology index, an average peak voltage of limb leads, and a high-frequency morphology index corresponding to each resting lead. Among them, the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead, the average peak voltage of the limb leads is the average value of the peak voltage of each limb lead, and the resting leads include limb leads and chest leads. It can be understood that the first risk assessment feature can also include the sixth target lead number, which is used to qualitatively analyze the myocarditis situation. The sixth target lead number is the number of resting leads whose corresponding peak number exceeds the first number threshold and the corresponding lead positive indicator indicates a positive result. The first number threshold is, for example, 3. The second risk assessment feature includes the QRS duration, the average peak voltage of the limb leads, and the high-frequency morphology index corresponding to each resting lead. It can also include the number of resting positive leads for quantitative analysis of heart failure risk, and can also include the sixth target lead number for qualitative analysis of heart failure.

[0139] In this embodiment, the risk assessment features corresponding to sudden cardiac death risk include the QRS duration and the high-frequency morphology index corresponding to each resting lead. They also include at least one of the following: the number of resting positive leads, an arrhythmia assessment index, a target peak voltage, and a tachycardia assessment index, for quantitatively analyzing the risk of sudden cardiac death. They may also include the number of seventh target leads for qualitatively analyzing the risk of sudden cardiac death. They may also include a QRS fragmentation index for more accurate qualitative analysis in combination with the number of seventh target leads. The arrhythmia assessment index includes frequent arrhythmias and sporadic arrhythmias. The target peak voltage is the minimum value of the peak voltages corresponding to each resting lead. The tachycardia assessment index indicates the possibility of ventricular tachycardia. The number of seventh leads is the number of resting leads with a corresponding peak number greater than or equal to a second threshold. The second threshold is, for example, 4. The arrhythmia assessment index and the tachycardia assessment index are determined based on a low-frequency electrocardiogram derived from the resting ECG signal. The QRS fragmentation index is used to indicate whether there is QRS fragmentation in the low-frequency electrocardiogram.

[0140] In one embodiment, the ECG data includes a resting ECG signal and an exercise ECG signal. If the cardiac risk type includes the risk of coronary artery stenosis, the corresponding risk assessment feature is determined based on the exercise ECG signal. If the cardiac risk type includes the risk of myocardial injury, the corresponding risk assessment feature is determined based on the resting ECG signal. If the cardiac risk type includes the risk of sudden cardiac death, the corresponding risk assessment feature is determined based on the resting ECG signal and the exercise ECG signal. The risk assessment feature corresponding to the risk of coronary artery stenosis includes at least one of a first relative value of amplitude decrease and a first absolute value of amplitude decrease, which is used to quantitatively analyze the coronary artery stenosis. It may also include the number of exercise-positive leads, which is used to quantitatively analyze the myocardial ischemia. It may also include a second relative value of amplitude decrease, which is used to qualitatively analyze the coronary artery stenosis. The risk assessment feature corresponding to the risk of myocardial injury is consistent with that in the above-mentioned corresponding embodiments and will not be repeated here. The risk assessment features corresponding to the risk of sudden cardiac death include risk assessment features determined based on resting ECG signals and risk assessment features determined based on exercise ECG signals. The risk assessment features determined based on resting ECG signals are consistent with those in the above-mentioned corresponding embodiments and will not be repeated here. The risk assessment features determined based on exercise ECG signals include the number of motion-positive leads and may also include the relative value of the second amplitude decrease.

[0141] In the above embodiment, corresponding risk assessment features are determined according to the subject's cardiac risk type based on the electrocardiogram data, and the concern level corresponding to the cardiac risk type is further determined based on the risk assessment features for the doctor's reference, so that the doctor can more accurately identify the subject's cardiac health status in combination with clinical symptoms.

[0142] In one or more embodiments of the present application, the resting leads used to collect resting ECG signals during resting ECG detection and the motion leads used to collect motion ECG signals during load exercise ECG detection may be the same or different in type, quantity, and position, and are not specifically limited here.

[0143] In one embodiment, as shown in FIG2 , a schematic diagram of a high-frequency QRS waveform curve is provided. The high-frequency QRS waveform curve is used to characterize the change trend of the root mean square of the high-frequency component of the QRS complex of the subject over time during the entire stress exercise ECG test process, that is, it is used to reflect the energy change trend during the entire stress exercise ECG test process. FIG2 illustrates the high-frequency QRS waveform curve corresponding to the limb lead aVF. The horizontal axis is time, which corresponds to the detection time of the stress exercise ECG test process, and the unit is min (minutes). The vertical axis is the root mean square voltage (RMS voltage), which can also be understood as intensity or amplitude, and the unit is uV (microvolt). Among them, the relative value of the first amplitude drop and the absolute value of the first amplitude drop used to determine the positive indicator of the corresponding lead are 55% and 2.9uV, respectively.

[0144] In one embodiment, as shown in FIG3 , a schematic diagram of a high-frequency QRS envelope curve is provided. The high-frequency QRS envelope curve represents the shape of all high-frequency QRS complexes (the high-frequency components of the QRS complex) in the resting ECG signal, and is specifically presented as a single high-frequency QRS complex envelope curve. FIG3 illustrates the high-frequency QRS envelope curve corresponding to chest lead V5. The abscissa is time, corresponding to the duration of the QRS complex, in milliseconds, and the ordinate is voltage, in microvolts. The high-frequency morphology index of the corresponding lead is 7.1%.

[0145] As shown in FIG4 , in one embodiment, a cardiac risk assessment method is provided, which specifically includes the following steps:

[0146] S402, obtaining ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a stress exercise ECG test can be performed; the target ECG test includes at least a resting ECG test.

[0147] S404: If the target ECG test only includes resting ECG test, the ECG data includes the subject's age and resting ECG signal; and the high-frequency components of the QRS complex in the resting ECG signal are analyzed to obtain a high-frequency QRS envelope curve.

[0148] S406, determine the first resting reference feature and the second resting reference feature based on the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target number of leads, the second target number of leads, the target high-frequency morphology index and the target root mean square voltage; the first target number of leads refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target number of leads refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target number of leads and the target high-frequency morphology index.

[0149] S408: Determine a first risk assessment level according to the first resting reference feature.

[0150] S410: Determine a second risk assessment level according to a second resting reference feature.

[0151] S412, determining a cardiac risk assessment level and a cardiac risk type based on the first risk assessment level and the second risk assessment level; the cardiac risk assessment level serves as a reference indicator for triaging the subject; and the cardiac risk type serves as a reference indicator for assessing the possible risk type of the subject's heart.

[0152] S414, if the target ECG test also includes stress exercise ECG test, the ECG data includes the subject's age, resting ECG signal and exercise ECG signal; the high-frequency components of the QRS complex in the resting ECG signal are analyzed to obtain a high-frequency QRS envelope curve.

[0153] S416, determine the first resting reference feature and the second resting reference feature based on the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target number of leads, the second target number of leads, the target high-frequency morphology index and the target root mean square voltage; the first target number of leads refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target number of leads refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target number of leads and the target high-frequency morphology index.

[0154] S418, analyzing the high-frequency components of the QRS complex in the exercise electrocardiogram signal to obtain a high-frequency QRS waveform curve.

[0155] S420: Determine the maximum heart rate of the subject based on the exercise electrocardiogram signal.

[0156] S422, determining the first motion reference feature and the second motion reference feature based on the high-frequency QRS waveform curve, age and maximum heart rate; the first motion reference feature includes the number of motion-positive leads, the number of motion-critical leads, the number of third target leads, the number of fourth target leads and the number of fifth target leads, as well as the first relative amplitude decrease value and the second relative amplitude decrease value corresponding to each motion lead; the third target lead number refers to the number of motion leads whose corresponding first relative amplitude decrease value is greater than or equal to the first relative value threshold; the fourth target lead number refers to the number of motion leads whose corresponding high-frequency QRS waveform curve shows a downward and upward repeated fluctuation trend within the first time period; the fifth target lead number refers to the number of motion leads whose corresponding second relative amplitude decrease value is greater than or equal to the second relative value threshold; the second motion reference feature includes the number of motion-positive leads and the number of motion-critical leads, as well as the second relative amplitude decrease value corresponding to each motion lead.

[0157] S424: Determine a first risk assessment level according to the first resting reference feature.

[0158] S426: Determine a second risk assessment level according to the second rest reference feature and the second motion reference feature.

[0159] S428: Determine a third risk assessment level according to the first motion reference feature.

[0160] S430: Determine a cardiac risk assessment level and a cardiac risk type according to the first risk assessment level, the second risk assessment level, and the third risk assessment level.

[0161] S432: Determine corresponding risk assessment features according to the cardiac risk type based on the ECG data.

[0162] S434: Determine the level of concern for the corresponding cardiac risk type based on the risk assessment characteristics.

[0163] In the above embodiment, if the clinical indication indicates that the subject cannot undergo stress exercise ECG testing, the subject's age and the resting ECG signal collected during the resting ECG testing process are obtained and analyzed to obtain the first resting reference feature and the second resting reference feature, and then the corresponding cardiac risk assessment level and cardiac risk type are determined. If the clinical indication indicates that the subject can undergo stress exercise ECG testing, the subject's age and the resting ECG signal collected during the resting ECG testing process are obtained and analyzed to obtain the first resting reference feature and the second resting reference feature, and the age and the exercise ECG signal collected during the stress exercise ECG testing process are obtained and analyzed to obtain the first exercise reference feature and the second exercise reference feature, and the corresponding cardiac risk assessment level and cardiac risk type are obtained by comprehensively considering each reference feature. By outputting a cardiac risk assessment level that represents the risk of the subject developing heart problems, as well as a cardiac risk type that represents the possible risk type for the heart, for the doctor's reference, the doctor can efficiently and accurately identify the subject's cardiac health status based on clinical symptoms, and provide further diagnosis and / or testing reference recommendations to achieve general screening, guidance, and triage of the subject. Based on the ECG data, the corresponding risk assessment features are determined according to the subject's cardiac risk type, and then the corresponding level of concern for the cardiac risk type is determined for the doctor's reference, so that the doctor can more accurately identify the subject's cardiac health status based on clinical symptoms and provide further diagnosis and treatment reference recommendations.

[0164] It should be understood that, although the various steps in the flowcharts of Figures 1 and 4 are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in Figures 1 and 4 may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0165] In one embodiment, as shown in FIG5 , a cardiac risk assessment device 500 is provided, comprising: an acquisition module 501 , a feature determination module 502 , and a risk assessment module 503 , wherein:

[0166] Acquisition module 501 is used to acquire ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a stress exercise ECG test can be performed; the target ECG test includes at least a resting ECG test;

[0167] A feature determination module 502 is configured to analyze the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain corresponding reference features;

[0168] The risk assessment module 503 is used to determine the cardiac risk assessment level and cardiac risk type based on the reference characteristics; the cardiac risk assessment level serves as a reference indicator for triaging the subject; and the cardiac risk type serves as a reference indicator for assessing the possible risk type of the subject's heart.

[0169] In one embodiment, if the target ECG detection only includes resting ECG detection, the ECG data includes the subject's age and resting ECG signal, and the reference feature includes a first resting reference feature; the feature determination module 502 is also used to analyze the high-frequency components of the QRS wave group in the resting ECG signal to obtain a high-frequency QRS envelope curve; determine the first resting reference feature based on the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold.

[0170] In one embodiment, the reference feature also includes a second resting reference feature; the feature determination module 502 is further used to determine the second resting reference feature based on the high-frequency QRS envelope curve and age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the number of first target leads; the risk assessment module 503 is further used to determine a first risk assessment level based on the first resting reference feature; determine a second risk assessment level based on the second resting reference feature; and determine a cardiac risk assessment level and a cardiac risk type based on the first risk assessment level and the second risk assessment level.

[0171] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes a target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead.

[0172] In one embodiment, if the target ECG detection also includes load exercise ECG detection, the ECG data includes the age, resting ECG signal and exercise ECG signal of the subject, and the reference features include a first resting reference feature and a first exercise reference feature; the feature determination module 502 is also used to analyze the high-frequency components of the QRS wave group in the resting ECG signal to obtain a high-frequency QRS envelope curve; determine the first resting reference feature based on the high-frequency QRS envelope curve and age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the first target lead number and the second target lead number; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold Quantity; analyze the high-frequency components of the QRS wave group in the exercise ECG signal to obtain a high-frequency QRS waveform curve; determine the maximum heart rate of the subject based on the exercise ECG signal; determine the first motion reference feature based on the high-frequency QRS waveform curve, age and maximum heart rate; the first motion reference feature includes the number of motion-positive leads, the number of motion-critical leads, the number of third target leads, the number of fourth target leads and the number of fifth target leads; the number of third target leads refers to the number of motion leads whose corresponding first amplitude decrease relative value is greater than or equal to the first relative value threshold; the number of fourth target leads refers to the number of motion leads whose corresponding high-frequency QRS waveform curve shows a downward and upward repeated fluctuation trend within the first time period; the number of fifth target leads refers to the number of motion leads whose corresponding second amplitude decrease relative value is greater than or equal to the second relative value threshold.

[0173] In one embodiment, the reference feature also includes a second resting reference feature and a second motion reference feature; the feature determination module 502 is further used to determine the second resting reference feature based on the high-frequency QRS envelope curve and age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the number of first target leads; the second motion reference feature is determined based on the high-frequency QRS waveform curve, age and maximum heart rate; the second motion reference feature includes the number of motion-positive leads and the number of motion-critical leads; the risk assessment module 503 is further used to determine a first risk assessment level based on the first resting reference feature; determine a second risk assessment level based on the second resting reference feature and the second motion reference feature; determine a third risk assessment level based on the first motion reference feature; and determine a cardiac risk assessment level and a cardiac risk type based on the first risk assessment level, the second risk assessment level and the third risk assessment level.

[0174] In one embodiment, the first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes a target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology index corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltage corresponding to each resting lead; the first motion reference feature also includes a first amplitude decrease relative value and a second amplitude decrease relative value corresponding to each motion lead; the second motion reference feature also includes a second amplitude decrease relative value corresponding to each motion lead.

[0175] In one embodiment, the feature determination module 502 is further configured to determine corresponding risk assessment features according to the cardiac risk type based on the ECG data; and the risk assessment module 503 is further configured to determine the level of concern of the corresponding cardiac risk type based on the risk assessment features.

[0176] The specific definitions of the cardiac risk assessment device can be found in the definitions of the cardiac risk assessment method above and will not be further elaborated here. Each module in the aforementioned cardiac risk assessment device may be implemented in whole or in part via software, hardware, or a combination thereof. Each of these modules may be embedded in or independent of a processor within a computer device in hardware form, or may be stored in a computer device memory in software form, allowing the processor to invoke and execute the corresponding operations of each module.

[0177] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG6 . The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium. The database of the computer device is used to store electrocardiogram data corresponding to target electrocardiogram detection that matches clinical indications. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer-readable instructions are executed by the processor, a cardiac risk assessment method is implemented.

[0178] Those skilled in the art will understand that the structure shown in FIG6 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0179] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the cardiac risk assessment method provided in any embodiment of the present application are implemented.

[0180] In one embodiment, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the steps of the cardiac risk assessment method provided in any embodiment of the present application are implemented.

[0181] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile computer-readable storage medium. When executed, the computer-readable instructions can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0182] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A cardiac risk assessment method, characterized in that: The method comprises: Acquire ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a load exercise ECG test can be performed; the target ECG test at least includes a resting ECG test; Analyzing the high frequency components of the QRS complex in the electrocardiogram data according to the target electrocardiogram detection to obtain corresponding reference features; Determining a cardiac risk assessment level and a cardiac risk type according to the reference characteristics; the cardiac risk assessment level is used as a reference indicator for triaging the subject; the cardiac risk type is used as a reference indicator for assessing the possible risk type of the subject's heart; Wherein, if the target ECG detection also includes load exercise ECG detection, the ECG data includes the age, resting ECG signal and exercise ECG signal of the subject, and the reference feature includes a first resting reference feature and a first exercise reference feature; the high-frequency components of the QRS complex in the ECG data are analyzed according to the target ECG detection to obtain the corresponding reference features, including: Analyzing the high-frequency components of the QRS complex in the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve; Determine a first resting reference feature according to the high-frequency QRS envelope curve and the age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the number of first target leads and the number of second target leads; the first target lead number refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target lead number refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold; Analyze the high-frequency components of the QRS complex in the exercise electrocardiogram signal to obtain a high-frequency QRS waveform curve; Determining the maximum heart rate of the subject according to the exercise electrocardiogram signal; A first motion reference feature is determined according to the high-frequency QRS waveform curve, the age and the maximum heart rate; the first motion reference feature includes the number of motion-positive leads, the number of motion-critical leads, the number of third target leads, the number of fourth target leads and the number of fifth target leads; the third target lead number refers to the number of motion leads whose corresponding first amplitude decrease relative value is greater than or equal to the first relative value threshold; the fourth target lead number refers to the number of motion leads whose corresponding high-frequency QRS waveform curve shows a trend of repeated decrease and increase within a first time period; the fifth target lead number refers to the number of motion leads whose corresponding second amplitude decrease relative value is greater than or equal to the second relative value threshold.

2. The method according to claim 1, characterized in that If the target ECG detection only includes resting ECG detection, the ECG data includes the age and resting ECG signal of the subject, and the reference feature includes a first resting reference feature; The step of analyzing the high frequency components of the QRS complex in the electrocardiogram data according to the target electrocardiogram detection to obtain corresponding reference features includes: Analyzing the high-frequency components of the QRS complex in the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve; A first resting reference feature is determined according to the high-frequency QRS envelope curve and the age; the first resting reference feature includes the number of resting positive leads, the number of resting critical leads, the number of first target leads and the number of second target leads; the first target number of leads refers to the number of resting leads whose corresponding high-frequency morphology index is greater than or equal to the first index threshold; the second target number of leads refers to the number of resting leads whose corresponding root mean square voltage is less than or equal to the first voltage threshold.

3. The method according to claim 2, characterized in that The reference feature also includes a second resting reference feature; the high-frequency components of the QRS complex in the electrocardiogram data are analyzed according to the target electrocardiogram detection to obtain the corresponding reference feature, and further includes: Determining a second resting reference feature according to the high-frequency QRS envelope curve and the age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the first target lead number; The step of determining the cardiac risk assessment level and the cardiac risk type according to the reference feature comprises: determining a first risk assessment level according to the first resting reference feature; determining a second risk assessment level according to the second resting reference feature; A cardiac risk assessment level and a cardiac risk type are determined according to the first risk assessment level and the second risk assessment level.

4. The method according to claim 3, characterized in that The first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes the target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology indexes corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltages corresponding to each resting lead.

5. The method according to claim 1, characterized in that The reference features also include a second resting reference feature and a second motion reference feature; the high-frequency components of the QRS complex in the electrocardiogram data are analyzed according to the target electrocardiogram detection to obtain corresponding reference features, and further include: Determining a second resting reference feature according to the high-frequency QRS envelope curve and the age; the second resting reference feature includes the number of resting positive leads, the number of resting critical leads and the first target lead number; Determining a second motion reference feature according to the high-frequency QRS waveform curve, the age and the maximum heart rate; the second motion reference feature includes the number of motion-positive leads and the number of motion-critical leads; The step of determining the cardiac risk assessment level and the cardiac risk type according to the reference feature comprises: determining a first risk assessment level according to the first resting reference feature; determining a second risk assessment level according to the second rest reference feature and the second motion reference feature; determining a third risk assessment level according to the first motion reference feature; A cardiac risk assessment level and a cardiac risk type are determined according to the first risk assessment level, the second risk assessment level, and the third risk assessment level.

6. The method according to claim 5, characterized in that The first resting reference feature also includes a target high-frequency morphology index and a target root mean square voltage; the second resting reference feature also includes the target high-frequency morphology index; the target high-frequency morphology index is the maximum value of the high-frequency morphology indexes corresponding to each resting lead; the target root mean square voltage is the minimum value of the root mean square voltages corresponding to each resting lead; the first motion reference feature also includes a first amplitude decrease relative value and a second amplitude decrease relative value corresponding to each motion lead; the second motion reference feature also includes a second amplitude decrease relative value corresponding to each motion lead.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Determining corresponding risk assessment features according to the cardiac risk type based on the electrocardiogram data; A level of concern for a corresponding cardiac risk type is determined based on the risk assessment characteristics.

8. A cardiac risk assessment device, characterized in that: The device comprises: An acquisition module, used to acquire ECG data corresponding to a target ECG test that matches a clinical indication; the clinical indication is used to indicate whether a load exercise ECG test can be performed; the target ECG test at least includes a resting ECG test; A feature determination module, used for analyzing the high-frequency components of the QRS complex in the ECG data according to the target ECG detection to obtain corresponding reference features; The risk assessment module is used to determine the cardiac risk assessment level and the cardiac risk type according to the reference characteristics; the cardiac risk assessment level is used as a reference indicator for triaging the subject; the cardiac risk type is used as a reference indicator for assessing the possible risk type of the subject's heart.

9. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that: When the processor executes the computer-readable instructions, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer readable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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