Methods, devices, systems and storage media for assessing cardiac health

Abstract

This disclosure relates to the field of medical instrument technology, specifically to a method, processing device, system, and computer-readable storage medium for assessing cardiac health status. It obtains a first maximum voltage and waveform categories and positive indicators for each lead of the first high-frequency QRS complex from exercise electrocardiogram (ECG) data. It also obtains a second maximum voltage and peak count, high-frequency morphology index, positive indicators, and QRS duration for each lead from the number of second high-frequency QRS complexes obtained from resting ECG data. Based on these data, it obtains cardiopulmonary function indices, vascular responsiveness indices, and cardiomyocyte viability indices. The cardiac health assessment result is determined based on these indices. This scheme provides a comprehensive assessment, overcoming the shortcomings of existing technologies that rely on "single-dimensional assessment," and meeting the clinical need for a holistic assessment of cardiac health.

Description

Technical Field

[0001] This application relates to the field of medical instrument technology, specifically to a method, processing device, system, and computer-readable storage medium for assessing cardiac health status. Background Technology

[0002] With the continuous improvement of people's living standards and the increasing work pressure, heart disease is becoming increasingly prevalent among younger people, and heart health issues are receiving more and more attention. Therefore, accurately identifying heart health status is a crucial issue.

[0003] Currently, there are methods for assessing cardiac health based on electrocardiograms (ECG). However, existing cardiac health assessments are relatively singular in basis, such as assessing only cardiopulmonary function or only vascular responsiveness, resulting in relatively low accuracy in assessing cardiac health. Summary of the Invention

[0004] The purpose of this application is to provide a method, processing device, system, and computer-readable storage medium for assessing cardiac health status.

[0005] To achieve the above objectives, the first aspect of this application provides a method for assessing cardiac health status, comprising: Acquire both exercise ECG data and resting ECG data output from at least one ECG lead; The first high-frequency QRS complex data were obtained from the exercise electrocardiogram data; The first maximum voltage and the waveform category and positive index of the first high-frequency QRS complex corresponding to each ECG lead are obtained based on the first high-frequency QRS complex data of all ECG leads. The second high-frequency QRS complex data were obtained from the resting electrocardiogram data; The second maximum voltage, the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration of each electrocardiogram lead are obtained from the second high-frequency QRS complex data corresponding to all electrocardiogram leads. Cardiopulmonary function is assessed based on the first maximum voltage and / or the second maximum voltage to obtain a cardiopulmonary function index; Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive index of second lead and QRS duration of each electrocardiogram lead, vascular responsiveness is assessed to obtain the vascular responsiveness index. Myocardial cell viability was assessed based on the number of peaks, high-frequency morphology index, and positive indicators in the second lead of each electrocardiogram lead to obtain the myocardial cell viability index; and The cardiac health assessment results are determined based on the cardiopulmonary function index, vascular responsiveness index, and myocardial cell vitality index.

[0006] In this embodiment of the application, cardiopulmonary function is assessed based on a first maximum voltage and a second maximum voltage to obtain a cardiopulmonary function index, including: The first maximum voltage and the second maximum voltage are normalized to obtain the first value and the second value, respectively. Assign a first weight and a second weight to the first and second values ​​respectively, with the first weight being greater than the second weight; The first and second values ​​are weighted and summed using the first and second weights to obtain the cardiopulmonary function index.

[0007] In this embodiment of the application, vascular responsiveness is assessed based on the risk of heart disease determined according to waveform type and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration corresponding to each electrocardiogram lead, to obtain a vascular responsiveness index, including: In cases where the risk of coronary artery disease is determined based on the waveform category, the relative decrease in amplitude and the absolute decrease in voltage between the maximum and minimum root mean square voltage values ​​within a preset time period are determined in the first high-frequency QRS complex data corresponding to each ECG lead. The preset time period includes a period before the exercise phase, the exercise phase, and a period after the exercise phase during the stress exercise ECG detection process, and the maximum root mean square voltage value is earlier than the minimum root mean square voltage value. The maximum relative amplitude decrease and the maximum absolute voltage decrease among the relative amplitude decrease and absolute voltage decrease values ​​corresponding to each electrocardiogram lead are determined as the target maximum relative amplitude decrease and the target maximum absolute voltage decrease values. The first risk level of coronary artery disease risk is determined based on the relative decrease in the target maximum amplitude and the absolute decrease in the target maximum voltage.

[0008] In this embodiment of the application, vascular responsiveness is assessed based on the risk of heart disease determined according to waveform type and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration corresponding to each electrocardiogram lead, to obtain a vascular responsiveness index, including: If the risk of myocardial bridging is determined based on the waveform category, the second risk level of myocardial bridging is determined based on the number of ECG leads corresponding to positive results indicated by the first lead positive index.

[0009] In this embodiment of the application, vascular responsiveness is assessed based on the risk of heart disease determined according to waveform type and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration corresponding to each electrocardiogram lead, to obtain a vascular responsiveness index, including: In cases where the risk of coronary microcirculation disorder is determined based on waveform category, the third risk level of the risk of coronary microcirculation disorder is determined based on the positive index of the second lead of the target second high-frequency QRS complex data; wherein, the electrocardiogram lead corresponding to the target second high-frequency QRS complex data corresponds to the target first high-frequency QRS complex data, the positive index of the first lead of the target first high-frequency QRS complex data indicates positivity, and there are no first waveform features indicating coronary artery lesions or second waveform features indicating myocardial bridging.

[0010] In this embodiment of the application, when the second lead positive index of the target second high-frequency QRS group data indicates a positive result, the third risk level is determined as the first value; If the second lead of the target second high-frequency QRS complex data indicates a negative result, the third risk level is determined as the second value, where the second value is less than the first value.

[0011] In this embodiment of the application, the vascular responsiveness is assessed based on the risk of heart disease determined according to the waveform category and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration of each electrocardiogram lead, to obtain a vascular responsiveness index, and further includes: If no coronary microcirculatory disturbance is determined based on the waveform category, the third risk level is determined as the third value if the first maximum voltage is greater than or equal to the voltage threshold or if the cardiopulmonary function index is greater than or equal to the index threshold, wherein the third value is less than the second value, and the first maximum voltage is determined based on the maximum value of the root mean square voltage in the first high-frequency QRS complex data corresponding to all ECG leads.

[0012] In this embodiment of the application, the vascular responsiveness is assessed based on the risk of heart disease determined according to the waveform category and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration of each electrocardiogram lead, to obtain a vascular responsiveness index, and further includes: If the first maximum voltage is greater than or equal to the voltage threshold, and the number of ECG leads corresponding to positive results in the second lead is less than a threshold, the third risk level is determined as the fourth value, where the fourth value is less than the third value.

[0013] In this embodiment of the application, vascular responsiveness is assessed based on the risk of heart disease determined according to waveform type and the risk of heart failure determined according to the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration corresponding to each electrocardiogram lead, to obtain a vascular responsiveness index, including: The risk of heart failure is determined based on the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead. The determination of heart failure risk based on the number of peaks in each electrocardiogram lead, high-frequency morphology index, positive indicators in lead 2, and QRS duration includes: The fourth risk level of heart failure is determined based on the presence of a specific heart failure feature and / or the number of heart failure features present in a plurality of heart failure features, including: QRS duration is greater than the time limit threshold; The number of peaks in the second high-frequency QRS complex data corresponding to at least one ECG lead is greater than or equal to a peak number threshold, and the amplitude voltage is less than a voltage threshold. The positive index of the second lead indicates that the number of positive electrocardiogram leads is greater than or equal to the first threshold. The number of ECG leads corresponding to a high-frequency morphology index greater than or equal to the high-frequency morphology index threshold is greater than or equal to the second quantity threshold.

[0014] In this embodiment of the application, the vascular responsiveness index is negatively correlated with the largest of the first risk level, the second risk level, the third risk level, and the fourth risk level, or the vascular responsiveness index is negatively correlated with the result obtained by weighted summation of the first risk level, the second risk level, the third risk level, and the fourth risk level.

[0015] In this embodiment of the application, cardiomyocyte viability is assessed based on the number of peaks, high-frequency morphology index, and positive indicators in the second lead of each electrocardiogram lead to obtain a cardiomyocyte viability index, including: The value of the first sub-indicator is determined based on the high-frequency morphological index; The value of the second sub-index is determined based on the number of ECG leads corresponding to a positive result indicated by the positive index of the second lead. The third sub-index value is determined based on the maximum number of peaks among all electrocardiogram leads. The cardiomyocyte vitality index is determined based on the first sub-index value, the second sub-index value, and the third sub-index value. The cardiomyocyte vitality index is negatively correlated with the result obtained by weighted summation of the first sub-index value, the second sub-index value, and the third sub-index value.

[0016] In this embodiment of the application, the method for assessing cardiac health status further includes: The cardiac health assessment results were revised based on the baseline questionnaire factors of the test subjects to obtain the revised cardiac health assessment results, wherein the baseline questionnaire factors included at least one of the following: Past medical history; History of taking vasodilators; Gender and menstrual cycle; Detection time.

[0017] In this application embodiment, determining the risk of coronary artery disease based on waveform type includes: The first point sequence corresponding to each ECG lead is obtained based on the first high-frequency QRS complex data corresponding to each ECG lead. The first point sequence includes multiple sampling points arranged in time sequence. For any ECG lead corresponding to the first point sequence, in one iteration, any one of the multiple sampling points in the first point sequence is taken as the current reference sampling point, and the sampling points in the first preset time interval starting from the current reference sampling point are traversed to determine whether there are first sampling points and second sampling points that meet preset conditions in the first preset time interval, wherein the first sampling point is earlier than the second sampling point in time. If it is determined that there are first sampling points and second sampling points that meet the preset conditions, the first waveform feature of the first high-frequency QRS complex data corresponding to the arbitrary electrocardiogram lead is determined to indicate coronary artery lesions. If it is determined that there is no first sampling point or second sampling point that meets the preset conditions, the current traversal loop ends, and other sampling points in the first point sequence other than the current reference sampling point are determined as new reference sampling points; For each new reference sampling point, the next traversal loop is performed until it is determined that there is a first sampling point and a second sampling point that meet the preset conditions, or all the sampling points that need to be traversed in the first point sequence have been traversed.

[0018] In this embodiment of the application, determining the risk of coronary artery disease based on waveform type further includes: If, after all the sampling points that need to be traversed in the first point sequence have been traversed, there are no first sampling points or second sampling points that meet the preset conditions, it is determined that the corresponding first high-frequency QRS complex data does not contain the first waveform feature indicating coronary artery lesions. If the first high-frequency QRS complex data corresponding to all electrocardiogram leads do not show the first waveform characteristics that indicate coronary artery disease, then the risk of coronary artery disease is determined to be absent.

[0019] In this embodiment of the application, the preset conditions include: The relative decrease in amplitude between the first sampling point and the second sampling point reaches a first preset threshold, and the degree of voltage decrease between the first sampling point and the second sampling point reaches a second preset threshold; or The relative value of the amplitude decrease between the first sampling point and the second sampling point reaches the first preset threshold, or the degree of voltage decrease between the first sampling point and the second sampling point reaches the second preset threshold.

[0020] In this embodiment of the application, determining the risk of myocardial bridging based on waveform type includes: Candidate first high-frequency QRS complex data are determined from the first high-frequency QRS complex data corresponding to each electrocardiogram lead. The candidate first high-frequency QRS complex data does not have the first waveform characteristics that indicate coronary artery lesions. The second point sequence is obtained based on the candidate first high-frequency QRS group data. This second point sequence includes multiple sampling points arranged in time sequence. For any candidate first high-frequency QRS group data corresponding to the second point sequence, in one traversal loop, any one of the multiple sampling points in the second point sequence is taken as the current reference sampling point, and the sampling points in the second preset time interval starting from the current reference sampling point are traversed to determine whether there are a third sampling point and a fourth sampling point that satisfy another preset condition in the second preset time interval, wherein the third sampling point is earlier than the fourth sampling point in time. If it is determined that there are third and fourth sampling points that meet another preset condition, the second waveform feature indicating myocardial bridging is determined to exist in the arbitrary candidate first high-frequency QRS complex data. If it is determined that there are no third or fourth sampling points that satisfy another preset condition, the current traversal loop ends, and other sampling points in the second point sequence other than the current reference sampling point are determined as new reference sampling points; For the new reference sampling point, perform the next traversal loop until it is determined that there are a third and fourth sampling points that satisfy another preset condition, or all the sampling points that need to be traversed in the second point sequence have been traversed.

[0021] In this embodiment of the application, determining the risk of myocardial bridging based on waveform type further includes: If, after all the sampling points that need to be traversed in the second point sequence have been traversed, there are no third and fourth sampling points that meet another preset condition, it is determined that the corresponding candidate first high-frequency QRS complex data does not have the second waveform feature indicating myocardial bridging. If no second waveform feature indicating myocardial bridging is found in all candidate first high-frequency QRS complex data, then myocardial bridging is determined to be absent.

[0022] In this embodiment of the application, another preset condition includes: The relative decrease in amplitude between the third and fourth sampling points reaches the third preset threshold, and the degree of voltage decrease between the third and fourth sampling points reaches the fourth preset threshold, and the root mean square voltage of each sampling point between the third and fourth sampling points shows a continuous decreasing trend; or The relative value of the amplitude decrease between the third and fourth sampling points reaches the third preset threshold or the degree of voltage decrease between the third and fourth sampling points reaches the fourth preset threshold, and the root mean square voltage of each sampling point between the third and fourth sampling points shows a continuous downward trend.

[0023] In this application embodiment, determining the risk of coronary microcirculation disorder based on waveform type includes: Determine whether there is a target first high-frequency QRS complex data in the first high-frequency QRS complex data corresponding to the positive indicator of the first lead, and whether the target first high-frequency QRS complex data does not have the first waveform feature and the second waveform feature. Given the existence of the target first high-frequency QRS complex data, a risk of coronary microcirculatory disturbance is identified.

[0024] A second aspect of this application provides a processing apparatus including a processor and a memory, wherein instructions are stored in the memory, and the processor is configured to call and execute the instructions from the memory to implement the above-described cardiac health status assessment method.

[0025] A third aspect of this application provides a cardiac health status assessment system, comprising: An electrocardiogram (ECG) signal acquisition device includes at least one electrode forming at least one ECG lead for acquiring ECG signals from a subject; and The aforementioned processing device.

[0026] A fourth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned cardiac health assessment method.

[0027] Existing cardiac health assessment methods are mostly single-dimensional and fail to construct a holistic assessment system around the core cardiac physiological chain of "oxygen intake - oxygen transport - oxygen utilization," resulting in low assessment accuracy. The technical solution provided in this application takes oxygen transport efficiency as the core and establishes a three-dimensional closed-loop assessment system of "oxygen intake (cardiopulmonary function) - oxygen transport (vascular response) - oxygen utilization (myocardial cell vitality)," which fully matches the core chain of cardiac physiological function, provides comprehensive assessment dimensions, solves the deficiency of "single-dimensional assessment" in existing technologies, and meets the clinical need for "holistic assessment" of cardiac health.

[0028] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0029] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 An example flowchart of a cardiac health status assessment method according to an embodiment of this application is illustrated.

[0030] Figure 2A The diagram illustrates a high-frequency QRS waveform curve during motion, showing a steep drop in waveform after 3 minutes of motion.

[0031] Figure 2B An example flowchart illustrating a method for determining whether a first waveform feature exists in first high-frequency QRS complex data (to determine the likelihood of coronary artery lesions) according to an embodiment of this application is shown.

[0032] Figure 3 An example flowchart illustrating a method for determining the likelihood of coronary artery disease according to another embodiment of this application is shown.

[0033] Figure 4 An example flowchart illustrating a method for determining the likelihood of coronary artery disease according to another embodiment of this application is shown.

[0034] Figure 5 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to an embodiment of this application is shown.

[0035] Figure 6 An example flowchart illustrating a method for determining whether a second waveform feature exists in a first high-frequency QRS complex (to determine the likelihood of myocardial bridging) according to an embodiment of this application is shown.

[0036] Figure 7 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to another embodiment of this application is shown.

[0037] Figure 8 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to another embodiment of this application is shown.

[0038] Figure 9 An example flowchart illustrating a method for determining the likelihood of the presence of a myocardial bridging according to yet another embodiment of this application is shown.

[0039] Figure 10 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to yet another embodiment of this application is shown.

[0040] Figure 11 An example flowchart illustrating a method for determining the likelihood of coronary microcirculatory disturbance according to an embodiment of this application is shown.

[0041] Figure 12 An example block diagram of a cardiac health status assessment system according to an embodiment of this application is shown schematically. Detailed Implementation

[0042] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0043] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0044] The cardiac health status assessment method provided in this application can be applied to a terminal, a server, or an interactive system including both a terminal and a server, and is implemented through the interaction between the terminal and the server. No specific limitations are made here. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, ECG monitoring devices, and portable wearable devices. The server can be a standalone server or a server cluster consisting of multiple servers.

[0045] In the embodiments of this application, "exercise ECG data" or its equivalents refer to ECG data collected from the subject during exercise ECG testing. Exercise ECG testing increases the cardiac load by a certain amount of exercise to collect ECG data from the subject, and analyzes the subject's cardiac health status based on the collected ECG data. It is widely used in the detection of heart and cardiovascular diseases. Exercise ECG data may include multiple QRS complexes reflecting changes in the depolarization potential and time of the left and right ventricles. Each QRS complex is a set of Q waves, R waves, and S waves in the ECG. Based on the QRS complexes in the exercise ECG data, corresponding high-frequency QRS waveform curves can be obtained. The high-frequency QRS waveform curve can essentially be a curve obtained by connecting multiple sampling points in time sequence. The high-frequency QRS waveform curve is also called the high-frequency QRS time-intensity curve, which can be used to characterize the root mean square voltage of the high-frequency components of the subject's QRS complex over time during the entire exercise ECG testing process, that is, to reflect the energy change trend during the entire exercise ECG testing process. The high-frequency QRS waveform curve is presented through the high-frequency QRS waveform diagram. In the high-frequency QRS waveform diagram, the horizontal axis can be time, which corresponds to the detection time of the load exercise ECG detection process, and the unit can be min (minute). The vertical axis can be the root mean square voltage (RMS voltage), which can also be understood as intensity or amplitude, and the unit can be uV (microvolt).

[0046] In one embodiment, the stress-exertion ECG detection process includes multiple stages, specifically three stages: a resting stage, an exercise stage, and a recovery stage. The exercise ECG data includes ECG data from each stage. It is understood that the stage division is not limited to this and can be adjusted according to actual circumstances. In this embodiment, the pre-exercise stage is the resting stage, the exercise stage is the exercise stage, and the post-exercise stage is the recovery stage.

[0047] In the embodiments of this application, "resting ECG data" or its equivalents can refer to ECG data collected during resting ECG testing. The subject is in a resting state during the resting ECG testing process. Resting ECG data may include multiple QRS complexes. High-frequency QRS complex data can be obtained by sequentially aligning, averaging, and high-frequency filtering the QRS complexes in the resting ECG data; alternatively, high-frequency QRS complex data can be obtained by sequentially performing high-frequency filtering, alignment, and averaging the QRS complexes in the resting ECG data; or, high-frequency ECG data can be obtained by analyzing the resting ECG data, extracting it, and then sequentially aligning and averaging the QRS complexes in the high-frequency ECG data. No specific limitations are made here. High-frequency QRS complex data corresponds to a high-frequency QRS envelope curve; a corresponding high-frequency QRS envelope curve can be formed based on the high-frequency QRS complex data. Therefore, it is evident that a corresponding high-frequency QRS envelope curve can be obtained by processing resting ECG data.

[0048] In the embodiments of this application, "conventional ECG data" or its equivalents can refer to ECG data that can be obtained from conventional electrocardiogram (ECG) signals. In the embodiments of this application, conventional ECG data can be obtained by analyzing and processing resting ECG data. For example, resting ECG data can be low-pass filtered to remove high-frequency components and retain low-frequency effective ECG components. The result after this filtering process is conventional ECG data.

[0049] Figure 1 An example flowchart illustrating a cardiac health status assessment method according to an embodiment of this application is shown schematically. Specifically, as Figure 1 As shown, a method for assessing cardiac health status may include the following steps.

[0050] In step S101, exercise ECG data and resting ECG data output through at least one ECG lead are acquired respectively.

[0051] Specifically, during electrocardiogram (ECG) acquisition, at least one electrode pad can be used for signal acquisition on the subject. For example, using 10 electrode pads, the pads can be distributed on the chest and limbs, forming 12 ECG leads (such as V1, V2, V3, V4, V5, V6, I, II, III, aVL, aVF, and aVR), corresponding to 12 sets of ECG data output. It is understood that 10 electrode pads are only an example and are not intended to limit the specific number of electrode pads; more or fewer electrode pads can be used according to actual needs. Acquiring ECG signals from subjects undergoing exercise yields exercise ECG data, while acquiring ECG signals from subjects at rest yields resting ECG data and conventional ECG data.

[0052] In step S102, the first high-frequency QRS complex data is obtained based on the exercise electrocardiogram data.

[0053] In step S103, the first maximum voltage and the waveform category and positive index of the first high-frequency QRS complex corresponding to each electrocardiogram lead are obtained based on the first high-frequency QRS complex data corresponding to all electrocardiogram leads.

[0054] Specifically, in this embodiment, exercise ECG data can be analyzed and processed to obtain first high-frequency QRS complex data. Specifically, this can be achieved by analyzing the high-frequency components of the QRS complex in the exercise ECG data. More specifically, the exercise ECG data includes ECGs (electrocardiograms) corresponding to each heartbeat during the entire exercise ECG testing process, and the ECGs include QRS complexes. The exercise ECG data is divided into multiple subsets according to the time sequence and a preset step size using a window function. Each subset includes ECGs corresponding to multiple heartbeats. For each subset of ECG data, the ECG or QRS complexes corresponding to the multiple heartbeats included are sequentially aligned, averaged, and bandpass filtered to obtain the corresponding high-frequency QRS complexes (the high-frequency bands of the QRS complexes). The root mean square (RMS) of these high-frequency QRS complexes is then calculated to obtain the corresponding RMS voltage, which serves as the RMS voltage for that subset of ECG data. This yields a time-intensity data point set arranged chronologically, with each data point corresponding to a time and a RMS voltage (or intensity or amplitude). The high-frequency QRS complex data can include this set of data points (hereinafter referred to as the first high-frequency QRS complex data for ease of distinction). Based on the time-intensity data point set, the corresponding high-frequency QRS time-intensity curve can be obtained; in other words, the visualization of this time-intensity data point set is the aforementioned high-frequency QRS time-intensity curve.

[0055] It is understandable that the window length and preset movement step size of the window function can be customized according to actual needs. For example, the window length can be set to 10 seconds, and the preset movement step size can be 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 timing sequence, it refers to the order of detection time according to the signal acquisition time / the sequence of detection time in the load exercise ECG detection process.

[0056] Specifically, the first maximum voltage can be the maximum voltage value among the first high-frequency QRS complex data corresponding to all ECG leads. In one example, the first maximum voltage can be the voltage value obtained by rounding up to an even number of the maximum RMS voltage values ​​among the first high-frequency QRS complex data corresponding to all ECG leads for a single exercise stress test. In another example, the maximum RMS voltage value among the first high-frequency QRS complex data corresponding to all ECG leads can also be used as the first maximum voltage.

[0057] Specifically, a lead-positive index (hereinafter referred to as the first lead positive index) corresponding to each electrocardiogram lead can be obtained based on the first high-frequency QRS complex data. The first lead positive index can be obtained using methods disclosed in the prior art. For example, in one example, the relative value of amplitude decrease and the absolute value of amplitude can be calculated based on the first high-frequency QRS complex data to form the first lead positive index. A predefined function can be used to process the first high-frequency QRS complex data to calculate the relative value of amplitude decrease and the absolute value of amplitude between two reference points where the RMS voltage decreases most and fastest in the high-frequency QRS waveform during, before, and after exercise. The absolute value of amplitude is obtained by subtracting the RMS voltages of the two reference points, and the relative value of amplitude decrease is calculated as a percentage of the absolute value of amplitude relative to the RMS voltage value of the reference point with the highest RMS voltage. The relative value of amplitude decrease and the absolute value of amplitude are primarily indicators for assessing changes in cardiac blood flow during human exercise.

[0058] Waveform categories are used to assess or identify the potential risk of different types of heart disease, such as the risk of coronary artery disease (including coronary stenosis and coronary artery sclerosis), myocardial bridging, and coronary microcirculatory disturbances. Explanations and / or descriptions of waveform categories will be elaborated below.

[0059] In step S104, the second high-frequency QRS complex data is obtained based on the resting electrocardiogram data.

[0060] In step S105, the second maximum voltage, the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration are obtained based on the second high-frequency QRS complex data corresponding to all ECG leads.

[0061] Specifically, resting ECG data can be analyzed to obtain high-frequency QRS complex data (hereinafter referred to as second high-frequency QRS complex data for ease of distinction). The specific operation for analyzing resting ECG data to obtain the corresponding high-frequency QRS complex data is described above and will not be repeated here. Therefore, the time-domain envelope of the complex formed when the second high-frequency QRS complex data is visualized is the aforementioned high-frequency QRS envelope curve. Positive indicators for each ECG lead can be obtained from the second high-frequency QRS complex data (hereinafter referred to as second lead positive indicators for ease of distinction). For example, in one example, the second high-frequency QRS envelope curve obtained based on the second high-frequency QRS complex data for each ECG lead can be analyzed. The total area of ​​each amplitude-reduced region on the high-frequency QRS envelope curve is taken as the first total area, and the total area below the high-frequency QRS envelope curve is taken as the second total area. The ratio of the first total area to the second total area is taken as the high-frequency morphological index corresponding to that resting lead. The positive indicators for each lead are determined according to the subject's age and the high-frequency morphological index corresponding to each resting lead. For example, if the age is ≥50 years old, the positive index of the second lead is determined to be positive when the high frequency morphology index is ≥8%; if the age is <50 years old, the positive index of the second lead is determined to be positive when the high frequency morphology index is ≥15%.

[0062] Specifically, the second maximum voltage can be the maximum voltage value among the second high-frequency QRS complex data (or high-frequency QRS envelope curves) corresponding to all ECG leads. In one example, the second maximum voltage can be the voltage value obtained by rounding up to an even number of the maximum voltage / amplitude values ​​among the second high-frequency QRS complex data (or second high-frequency QRS envelope curves) corresponding to all ECG leads. In another example, the maximum voltage value among the second high-frequency QRS complex data corresponding to all ECG leads can also be used as the second maximum voltage.

[0063] Specifically, the peak count refers to the total number of peaks in the second high-frequency QRS complex data (or on the second high-frequency QRS envelope curve obtained from the second high-frequency QRS complex data). The peak count is related to the myocardial cell viability corresponding to the corresponding electrocardiogram lead. A peak count of 1 (i.e., a single peak) indicates normal myocardial cell viability, while a higher peak count indicates a greater decrease in myocardial cell viability. When the peak count is greater than or equal to a first threshold, the waveform type is multi-peaked. The first threshold can be determined according to actual needs, for example, 4.

[0064] Specifically, the high-frequency morphological index (HFMI) and QRS duration can be obtained by analyzing the second high-frequency QRS complex data (or the second high-frequency QRS envelope curve). For example, the high-frequency morphological index can be obtained by calculating the ratio of the area of the amplitude attenuation region (RAZ) of the second high-frequency QRS complex data (or the second high-frequency QRS envelope curve) to the area under the envelope curve, which can reflect the degree of myocardial injury. The specific calculation method of the high-frequency morphological index can be: for each electrocardiogram lead, analyze the corresponding second high-frequency QRS envelope curve of this electrocardiogram lead, obtain the total area of each amplitude attenuation region (also called the amplitude reduction region) on this second high-frequency QRS envelope curve as the first total area, and the total area under this second high-frequency QRS envelope curve as the second total area, and take the ratio of the first total area to the second total area as the high-frequency morphological index corresponding to this electrocardiogram lead. The QRS duration is the duration from the start point to the end point of the QRS complex, and the prolongation of the QRS duration is related to conduction block. The prolongation of the QRS duration is an important predictor of sudden cardiac death, and the longer the QRS duration, the higher the risk of sudden cardiac death.

[0065] In step S106, the cardiopulmonary function is evaluated according to the first maximum voltage and / or the second maximum voltage to obtain the cardiopulmonary function index.

[0066] Specifically, in the embodiment of the present application, the first maximum voltage and / or the second maximum voltage can be normalized to obtain the normalized first value and second value. For example, the values of the first maximum voltage and / or the second maximum voltage (the unit is, for example, μV) can be mapped to the interval [0, 1], but those skilled in the art can understand that the voltage value can be mapped to other intervals.

[0067] In one example, the following normalization process can be performed on the first maximum voltage (which can also be called the exercise maximum voltage, denoted as Stress_maxV): If Stress_maxV ≤ 6 μV, then Stress_score = 0.3 (weak, indicating insufficient cardiopulmonary reserve); If 6 μV < Stress_maxV ≤ 8 μV, then Stress_score = 0.6 (normal); If 8 μV < Stress_maxV, then Stress_score = 1 (strong, indicating good cardiopulmonary reserve); Among them, Stress_score is the normalized first value.

[0068] Similarly, for the second maximum voltage (which can also be called the resting maximum voltage, denoted as Rest_maxV), the following normalization process can be performed: If Rest_maxV ≤ 10μV, then Rest_score = 0.3 (weak); If 10μV < Rest_maxV ≤ 14μV, then Rest_score = 0.6 (normal); If 14μV < Rest_maxV, then Rest_score = 1 (strong); Where, Stress_score is the second normalized value.

[0069] In one embodiment, the cardiopulmonary function can be evaluated according to the first maximum voltage or the second maximum voltage.

[0070] In one embodiment, if the cardiopulmonary function is evaluated according to the first maximum voltage and the second maximum voltage, considering that the first maximum voltage (obtained under the exercise load state) reflects the cardiopulmonary reserve capacity (which is the clinical core evaluation index), and the second maximum voltage (obtained under the resting state) reflects the basal state. The electrocardiogram response under the exercise state can better reflect the cardiopulmonary reserve function and oxygen intake efficiency, and has high clinical value. While the resting state provides a reference for the basal state and is greatly affected by individual basal differences, and its predictive value is relatively low. In this embodiment, different weights can be assigned to the first value and the second value respectively, and the first weight assigned to the first value can be greater than the second weight assigned to the second value. In one example, the value range of the first weight can be, for example, from 0.6 to 0.8, and preferably can be 0.7. The value range of the second weight can be, for example, from 0.2 to 0.4, and preferably can be 0.3.

[0071] The first weight and the second weight can be used to perform weighted summation on the first value and the second value to obtain the cardiopulmonary function index. Taking the first weight as 0.7 and the second weight as 0.3 as an example, the cardiopulmonary function index can be obtained according to the following formula: V1_index = 0.7*Stress_score + 0.3*Rest_score Where, V1_index is the obtained cardiopulmonary function index.

[0072] In step S107, based on the heart disease risk determined according to the waveform category and the heart failure risk determined according to the number of wave peaks, high-frequency morphology index, positive index of the second lead, and QRS duration corresponding to each electrocardiogram lead, the vascular response ability is evaluated to obtain the vascular response ability index.

[0073] Specifically, in the embodiments of this application, different types of heart disease risks can be determined based on the waveform category, such as at least one of the following: coronary artery disease risk (e.g., including coronary artery stenosis and coronary artery sclerosis), myocardial bridging risk, and coronary microcirculation disorder risk. After determining or identifying the specific risk type, the degree of risk can be further determined.

[0074] In this embodiment, the presence of coronary artery disease risk (e.g., coronary artery stenosis or coronary artery sclerosis) can be identified or determined based on the waveform category of the first high-frequency QRS complex data. This waveform category may include waveform features (first waveform features) that indicate coronary artery disease, such as a steep drop wave. The likelihood (risk) of coronary artery disease can be determined by judging whether the first high-frequency QRS complex data contains the first waveform feature.

[0075] The following describes in detail the method for determining or identifying whether the first high-frequency QRS group data has a first waveform feature (e.g., a steep drop wave).

[0076] Coronary artery disease can be categorized as large vessel disease of the coronary arteries, specifically including coronary artery stenosis and coronary artery sclerosis. When exercise electrocardiogram data indicates the possibility of coronary artery stenosis or coronary artery sclerosis, the presence of coronary artery disease is considered a possibility.

[0077] In one embodiment of this application, the method disclosed in Chinese Patent Application No. CN114742114A, filed by the applicant of this application, can be used to determine whether a first waveform feature (e.g., a steep drop wave) exists, thereby judging the possibility of coronary artery stenosis. This application discloses a high-frequency QRS waveform curve analysis method, including acquiring a high-frequency QRS waveform curve (first high-frequency QRS waveform curve) corresponding to exercise electrocardiogram data; selecting the first high-frequency QRS waveform curve within a first time period as a first reference waveform curve; selecting the point with the minimum root-mean-square voltage from the first reference waveform curve as a first reference point, and the point earlier than the first reference point and with the largest root-mean-square voltage as a second reference point; determining a first amplitude decrease relative value based on the root-mean-square voltages of the first and second reference points; if the first amplitude decrease relative value is greater than or equal to a first preset threshold, determining that the first high-frequency QRS waveform curve meets a preset condition, i.e., the high-frequency QRS waveform curve has waveform features indicative of coronary artery stenosis, thereby judging or determining the possibility of coronary artery stenosis.

[0078] The high-frequency QRS waveform analysis method disclosed in the above embodiments mainly focuses on whether there is a situation where the relative value of the first amplitude decrease is greater than or equal to a first preset threshold in the QRS waveform curve segment corresponding to the first 3 minutes of exercise. That is, it mainly determines whether there is a steep drop waveform in the high-frequency QRS waveform curve during the first 3 minutes of exercise. If so, it determines that there is coronary artery stenosis. However, in reality, the location (time point) where the relative value of the first amplitude decrease is greater than or equal to the first preset threshold in the high-frequency QRS waveform curve may be after 3 minutes, that is, the high-frequency QRS waveform curve shows a steep drop waveform after 3 minutes of exercise (e.g., Figure 2A As shown in the diagram, the method in the above embodiment may overlook this situation and fail to detect the presence of coronary artery stenosis. Furthermore, this method may also overlook the identification of the possibility of coronary artery sclerosis.

[0079] In view of this, the inventors of this application, through further research, propose an innovative method for judging the possibility of coronary artery lesions by determining the presence of a first waveform feature. Compared with the method provided in the previously disclosed embodiments, the method provided in this embodiment can improve the accuracy of judging the possibility of coronary artery stenosis and can also judge the possibility of coronary artery sclerosis.

[0080] In a preferred embodiment of this application, the general inventive concept for determining the presence of coronary artery lesions may include obtaining first high-frequency QRS complex data based on exercise electrocardiogram data. The first high-frequency QRS complex data may include a set of data points arranged in chronological order (i.e., sequential time), where each data point corresponds to a time point (timestamp) and an amplitude value (or intensity value), which may be, for example, an RMS voltage value. The first high-frequency QRS waveform curve can essentially be a curve obtained by connecting these data points in chronological order, or it can be a curve obtained by smoothing out minor fluctuations.

[0081] A point sequence for each ECG lead can be obtained from the first high-frequency QRS complex data. This point sequence can include multiple data points sampled or selected from a set of data points in the first high-frequency QRS complex data at a set sampling period (sampling interval) or sampling frequency. Each sample point in the point sequence is traversed to determine whether the first high-frequency QRS complex data contains waveform features (first waveform features) indicative of coronary artery disease, such as a steep descent wave. If a steep descent wave is present, the possibility of coronary artery disease can be determined.

[0082] Figure 2B An example flowchart illustrating a method according to an embodiment of this application for determining whether a first waveform feature exists in first high-frequency QRS complex data (to determine the likelihood of coronary artery lesions). Figure 2BAs shown, specifically in this embodiment, the method may include the following steps.

[0083] In step S1031, the point sequence (which can be called the first high-frequency QRS time-intensity point sequence) corresponding to each ECG lead is obtained based on the first high-frequency QRS complex data corresponding to each ECG lead. The point sequence may include multiple sampling points arranged in time sequence.

[0084] In step S1032, for any point sequence corresponding to an ECG lead, any one of the multiple sampling points in the point sequence is used as a reference sampling point. The sampling points within a preset time interval starting from the reference sampling point are traversed to determine whether there exists an earlier first sampling point and a later second sampling point that satisfy preset conditions within the preset time interval. The sampling points within this preset time interval include the reference sampling point. In one example, the preset conditions may be that the relative amplitude decrease between the first and second sampling points reaches a first preset threshold and the voltage decrease between the first and second sampling points reaches a second preset threshold (hereinafter referred to as the first preset condition). In another example, the preset conditions may be that the relative amplitude decrease between the first and second sampling points reaches the first preset threshold or the voltage decrease between the first and second sampling points reaches the second preset threshold (hereinafter referred to as the second preset condition).

[0085] The relative value of the amplitude decrease can be, for example,: (RMS voltage at the first sampling point - RMS voltage at the second sampling point) / RMS voltage at the first sampling point. In one example, the degree of voltage decrease can be the absolute value of the voltage decrease, which can be the RMS voltage at the first sampling point minus (-) the RMS voltage at the second sampling point. In an alternative example, the degree of voltage decrease can be the ratio of the absolute value of the voltage decrease to the maximum voltage value, i.e., (RMS voltage at the first sampling point - RMS voltage at the second sampling point) / maximum voltage value. This maximum voltage value can be understood as the maximum power, which can be used to reflect the subject's maximum cardiac pumping function, i.e., the peak value of the first high-frequency QRS complex data (or the first high-frequency QRS waveform curve) will not exceed this maximum voltage value. This maximum voltage value can be determined based on the maximum value of the RMS voltage in the high-frequency QRS complex data corresponding to all ECG leads (e.g., the maximum RMS voltage rounded up to an even number). In this example, the second preset threshold can be in the form of a percentage, just like the first preset threshold.

[0086] In step S1033, if preset conditions are met, the possibility of coronary artery disease is determined. During the traversal, if a first sampling point and a second sampling point that meet the preset conditions appear, it indicates that the first high-frequency QRS complex data has waveform characteristics indicative of coronary artery disease, such as a steep drop wave. In this case, the possibility of coronary artery disease can be determined.

[0087] In step S1034, if the preset conditions are not met, step S1032 is repeated after changing the reference sampling point. In this step, if no first or second sampling point meeting the preset conditions appears after all sampling points within the preset time interval have been traversed, the current traversal loop ends. At this time, the reference sampling point can be changed, and the next traversal loop can be entered. For example, the sampling points adjacent to the current reference sampling point can be used as the new reference sampling point. The sampling points within the preset time interval starting from the new reference sampling point can be traversed using the new reference sampling point as the reference to determine whether there are first and second sampling points meeting the preset conditions. If there are first and second sampling points meeting the preset conditions, it indicates that the first high-frequency QRS complex data has waveform characteristics indicating coronary artery lesions, and the traversal ends. If no first or second sampling point meeting the preset conditions appears after the current traversal loop ends, the next traversal loop is entered until a first or second sampling point meeting the preset conditions appears, or all sampling points in the point sequence that need to be traversed have been traversed. If the preset conditions are still not met after all the sampling points to be traversed, then it is determined that there are no waveform characteristics indicating coronary artery lesions.

[0088] There are multiple ways or rules for traversing the sampling points. Figure 3 An example flowchart illustrating a method for determining the presence of coronary artery lesions according to an embodiment of this application is shown. Figure 3As shown, in this embodiment, for the first high-frequency QRS complex data corresponding to each ECG lead, the earliest sampling point in the time sequence can be used as the reference sampling point (which can be called the initial reference point). The next sampling point in the time sequence following the initial reference point is used as the traversal starting point, and traversal is performed according to the time sequence. The relative value of amplitude decrease and the degree of voltage decrease between the initial reference point (the first sampling point in this embodiment) and the currently traversed sampling point (the second sampling point in this embodiment) are calculated. It is then determined whether the initial reference point and the currently traversed sampling point meet preset conditions. If these two sampling points meet the preset conditions, it can be determined that the first high-frequency QRS complex data corresponding to the lead has waveform characteristics indicative of coronary artery disease, such as a steep drop wave, thus confirming the possibility of coronary artery disease, and the traversal stops. If the preset conditions are not met, it is determined whether the time interval between the currently traversed sampling point and the initial reference point exceeds a preset duration, and whether the currently traversed sampling point is the last sampling point in the point sequence. If the time interval between the current traversed sampling point and the initial reference point does not exceed a preset duration and the current traversed sampling point is not the last sampling point, then switch to the next sampling point of the current traversed sampling point, and use this next sampling point as the new current traversed sampling point. The same operation is then performed on this new current traversed sampling point. Specifically, the relative value of amplitude decrease and the degree of voltage decrease between the initial reference point and the new current traversed sampling point are calculated. It is then determined whether the initial reference point and the new current traversed sampling point meet preset conditions. If the preset conditions are met, it can be determined that the first high-frequency QRS complex data corresponding to this lead has a steep drop wave, thus confirming the possibility of coronary artery lesions, and the traversal stops. If the preset conditions are not met, the above operation can be repeated to traverse subsequent sampling points. If the preset conditions are still not met, then traversal can continue until the time interval between the traversed current traversed sampling point and the initial reference point reaches a preset duration (in this embodiment, the preset duration is the preset time interval mentioned above), or the current traversed sampling point is the last sampling point in the point sequence. If the time interval between the current traversed sampling point and the initial reference point reaches a preset duration, or if the current traversed sampling point is the last sampling point in the point sequence, then the process can switch to the next sampling point after the initial reference point, using this next sampling point as the reference sampling point, and proceeding with the next traversal loop in the same manner as described above. If the preset conditions are still not met, the traversal loop can continue until the preset conditions are met, or until all sampling points in the point sequence except the last sampling point have been used as reference sampling points to complete the traversal. In other words, if the preset conditions are still not met after the previous sampling point of the last sampling point has been used as the reference sampling point to complete the traversal, it indicates that there are no waveform characteristics indicative of coronary artery lesions, such as steep descent waves, in the first high-frequency QRS complex data corresponding to that ECG lead.

[0089] In this embodiment, the preset duration can range from, for example, 1 minute to 3 minutes. If the preset condition is that the relative amplitude decrease between the first sampling point and the second sampling point reaches a first preset threshold and the voltage decrease between the first sampling point and the second sampling point reaches a second preset threshold (first preset condition), then the first preset threshold can range from 30% to 40%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the second preset threshold can range from 1 uV to 2 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the second preset threshold can range from 10% to 20%.

[0090] If the preset condition is that the relative decrease in amplitude between the first and second sampling points reaches a first preset threshold, or the voltage decrease between the first and second sampling points reaches a second preset threshold (the first preset condition), then the value of the first preset threshold can range from 50% to 60%. In this case, the value of the second preset threshold is related to the preset duration. Generally speaking, the value of the second preset threshold is positively correlated with the preset duration; that is, the longer the preset duration, the larger the second preset threshold. For example, taking a preset duration ranging from 1 minute to 3 minutes as an example, if the preset duration is 1 minute, then when the voltage decrease is the absolute value of the voltage decrease, the value of the second preset threshold ranges from 1 uV to 2 uV; when the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, the value of the second preset threshold ranges from 10% to 20%. If the preset duration is 2 minutes, then when the voltage drop is the absolute value of the voltage drop, the second preset threshold ranges from 2 uV to 3 uV; when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the second preset threshold ranges from 20% to 30%. If the preset duration is 3 minutes, when the voltage drop is the absolute value of the voltage drop, the second preset threshold ranges from 3 uV to 4 uV; when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the second preset threshold ranges from 30% to 40%. Those skilled in the art will understand that the specific values ​​of the preset thresholds above are exemplary and are used to illustrate the correlation between the second preset threshold and the preset duration. For a shorter preset duration, using a larger second preset threshold may misidentify a steeply drooping wave as a non-steeply drooping wave; conversely, for a longer preset duration, using a smaller second preset threshold may misidentify a non-steeply drooping wave as a steeply drooping wave, thus reducing the accuracy of identification. The specific values ​​can be adjusted according to the actual application.

[0091] The above processing can be performed on the first high-frequency QRS complex data corresponding to all ECG leads. If at least one ECG lead's first high-frequency QRS complex data shows waveform characteristics indicative of coronary artery disease, such as a steep descent wave, then the possibility of coronary artery disease can be determined. Conversely, if none of the first high-frequency QRS complex data corresponding to all ECG leads shows waveform characteristics indicative of coronary artery disease, then the absence of coronary artery disease can be determined.

[0092] Although the above embodiments process multiple sampling points in a point sequence in a time sequence from early to late, those skilled in the art will understand that the above method is also applicable to multiple sampling points in a time sequence from late to early. In this case, the relative value of amplitude decrease and the degree of voltage decrease between the currently traversed sampling point and the reference sampling point are calculated (in this case, the currently traversed sampling point is earlier than the reference sampling point in time).

[0093] Figure 4 An example flowchart illustrating a method for determining the likelihood of coronary artery disease according to another embodiment of this application is shown. Figure 4As shown, in this embodiment, a window function can be preset. The window of this window function includes a start point and an end point, and the distance between the start point and the end point is the window length (in this embodiment, the window length is the preset time interval mentioned above). The value range of the window length can be, for example, 1 minute to 3 minutes. For the first high-frequency QRS complex data corresponding to each ECG lead, the window function is used to traverse the point sequence of the first high-frequency QRS complex data. Specifically, the earliest sampling point in the time sequence can be used as the starting point of the window. The sampling point with the largest RMS voltage (the first sampling point in this embodiment) and the sampling point with the smallest RMS voltage (the second sampling point in this embodiment) among the multiple sampling points in the point sequence included in the window can be determined. The relative value of the amplitude decrease and the degree of voltage decrease between the first sampling point and the second sampling point are calculated. It is determined whether the first sampling point and the second sampling point meet the preset conditions. If these two sampling points meet the preset conditions, it can be determined that the first high-frequency QRS complex data corresponding to that lead has a steep drop wave, thus confirming the possibility of coronary artery lesions, and the iteration stops. If the preset conditions are not met, the window of the window function can be slid backward by a preset step size, and the above operation can be repeated. If the preset conditions are still not met, the window can be slid backward by the preset step size again until the preset conditions are met, or until the end of the window reaches or exceeds the last sampling point in the point sequence. That is, if the preset conditions are not met after the window has slid by the preset step size and its end reaches or exceeds the last sampling point, it indicates that there are no waveform characteristics indicating coronary artery lesions, such as steep drops, in the first high-frequency QRS complex data of that ECG lead. In this embodiment, the preset step size can be, for example, an integer multiple of the time interval (e.g., sampling period) between two adjacent sampling points, preferably, the time interval between two adjacent sampling points, such as 10 seconds. Of course, those skilled in the art will understand that setting the preset step size to other values ​​is also feasible.

[0094] In this embodiment, if the preset condition is that the relative decrease in amplitude between the first sampling point and the second sampling point reaches a first preset threshold and the degree of voltage drop between the first sampling point and the second sampling point reaches a second preset threshold (first preset condition), then the value range of the first preset threshold can be 30% to 40%. In this case, if the degree of voltage drop is the absolute value of the voltage drop, then the value range of the second preset threshold can be 1 uV to 2 uV. If the degree of voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, then the value range of the second preset threshold can be 10% to 20%.

[0095] If the preset condition is that the relative decrease in amplitude between the first and second sampling points reaches a first preset threshold, or the voltage decrease between the first and second sampling points reaches a second preset threshold (the first preset condition), then the value of the first preset threshold can range from 50% to 60%. In this case, the value of the second preset threshold is related to the window length. Generally speaking, the value of the second preset threshold is positively correlated with the window length; that is, the longer the window length, the larger the second preset threshold. For example, taking a window length ranging from 1 minute to 3 minutes as an example, if the window length is 1 minute, and the voltage decrease is the absolute value of the voltage decrease, then the value of the second preset threshold ranges from 1 uV to 2 uV; if the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value of the second preset threshold ranges from 10% to 20%. If the window length is 2 minutes, the second preset threshold ranges from 2 uV to 3 uV when the voltage drop is the absolute value of the voltage drop, and from 20% to 30% when the voltage drop is the ratio of the absolute voltage drop to the maximum voltage value. If the window length is 3 minutes, the second preset threshold ranges from 3 uV to 4 uV when the voltage drop is the absolute value of the voltage drop, and from 30% to 40% when the voltage drop is the ratio of the absolute voltage drop to the maximum voltage value. Those skilled in the art will understand that the specific values ​​of the above preset thresholds are exemplary and intended to illustrate the correlation between the second preset threshold and the window length. For shorter window lengths, using a larger second preset threshold may misidentify steeply drooping waves as non-steeply drooping waves; for longer window lengths, using a smaller second preset threshold may misidentify non-steeply drooping waves as steeply drooping waves, thus reducing recognition accuracy. The specific values ​​can be adjusted according to actual applications.

[0096] The above processing can be performed on the first high-frequency QRS complex data corresponding to all ECG leads. If at least one ECG lead's first high-frequency QRS complex data shows waveform characteristics indicative of coronary artery disease, such as a steep descent wave, then the possibility of coronary artery disease can be determined. Conversely, if none of the first high-frequency QRS complex data corresponding to all ECG leads shows waveform characteristics indicative of coronary artery disease, then the absence of coronary artery disease can be determined.

[0097] Although the window function used in the above embodiments starts from the earliest temporally sampled point in the point sequence, those skilled in the art will understand that it can also start from the latest temporally sampled point in the point sequence.

[0098] Figure 5An example flowchart illustrating a method for determining the likelihood of coronary artery disease according to another embodiment of this application is shown. Figure 5 As shown, in this embodiment, a window function can be preset. The window of this window function includes a start point and an end point, and the distance between the start point and the end point is the window length (in this embodiment, the window length is the preset time interval mentioned above). The window length can range from, for example, 1 minute to 3 minutes. For the first high-frequency QRS complex data corresponding to each ECG lead, the start point of the window can be aligned with the earliest sampling point in the time sequence (i.e., these two points coincide in time). Using this first sampling point as a reference point, each sampling point within the window is traversed sequentially. Specifically, the relative value of amplitude decrease and the degree of voltage decrease between the reference point and the currently traversed sampling point within the window can be calculated. It is determined whether the reference point and the currently traversed sampling point meet preset conditions. If these two sampling points meet the preset conditions, it can be determined that the first high-frequency QRS complex data corresponding to the lead has waveform characteristics indicating coronary artery disease, such as a steep drop wave, and the possibility of coronary artery disease can be determined, and the traversal stops. If the preset conditions are not met, the next sampling point within the window is traversed. If the reference point and the currently traversed sampling point meet the preset conditions, the traversal stops; otherwise, it continues until the last sampling point within the traversal window. If the preset conditions are still not met after all sampling points within the window have been traversed, the traversal loop ends, and the window of the window function can be slid backward (time-wise backward) by a preset step size, and the above operation is repeated (the next traversal loop). If the preset conditions are still not met after the traversal loop ends, the window can be slid backward by the preset step size again until the preset conditions are met, or until the end of the window reaches or exceeds the last sampling point in the point sequence. That is, if the preset conditions are still not met after the window has slid by the preset step size and its end reaches or exceeds the last sampling point, it indicates that there are no waveform characteristics indicating coronary artery lesions, such as steep descent waves, for the first high-frequency QRS complex data of that ECG lead. In this embodiment, the preset step size can be, for example, an integer multiple of the time interval (e.g., sampling period) between two adjacent sampling points, preferably, the time interval between two adjacent sampling points, such as 10 seconds. Of course, those skilled in the field can understand that setting the preset step size to other values ​​is also feasible.

[0099] In this embodiment, if the preset condition is that the relative decrease in amplitude between the first sampling point and the second sampling point reaches a first preset threshold and the degree of voltage drop between the first sampling point and the second sampling point reaches a second preset threshold (first preset condition), then the value range of the first preset threshold can be 30% to 40%. In this case, if the degree of voltage drop is the absolute value of the voltage drop, then the value range of the second preset threshold can be 1 uV to 2 uV. If the degree of voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, then the value range of the second preset threshold can be 10% to 20%.

[0100] If the preset condition is that the relative decrease in amplitude between the first and second sampling points reaches a first preset threshold, or the voltage decrease between the first and second sampling points reaches a second preset threshold (the first preset condition), then the value of the first preset threshold can range from 50% to 60%. In this case, the value of the second preset threshold is related to the window length. Generally speaking, the value of the second preset threshold is positively correlated with the window length; that is, the longer the window length, the larger the second preset threshold. For example, taking a window length ranging from 1 minute to 3 minutes as an example, if the window length is 1 minute, and the voltage decrease is the absolute value of the voltage decrease, then the value of the second preset threshold ranges from 1 uV to 2 uV; if the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value of the second preset threshold ranges from 10% to 20%. If the window length is 2 minutes, the second preset threshold ranges from 2 uV to 3 uV when the voltage drop is the absolute value of the voltage drop, and from 20% to 30% when the voltage drop is the ratio of the absolute voltage drop to the maximum voltage value. If the window length is 3 minutes, the second preset threshold ranges from 3 uV to 4 uV when the voltage drop is the absolute value of the voltage drop, and from 30% to 40% when the voltage drop is the ratio of the absolute voltage drop to the maximum voltage value. Those skilled in the art will understand that the specific values ​​of the above preset thresholds are exemplary and intended to illustrate the correlation between the second preset threshold and the window length. For shorter window lengths, using a larger second preset threshold may misidentify steeply drooping waves as non-steeply drooping waves; for longer window lengths, using a smaller second preset threshold may misidentify non-steeply drooping waves as steeply drooping waves, thus reducing recognition accuracy. The specific values ​​can be adjusted according to actual applications.

[0101] The above processing can be performed on the first high-frequency QRS complex data corresponding to all ECG leads. If at least one ECG lead's first high-frequency QRS complex data shows waveform characteristics indicative of coronary artery disease, such as a steep descent wave, then the possibility of coronary artery disease can be determined. Conversely, if none of the first high-frequency QRS complex data corresponding to all ECG leads shows waveform characteristics indicative of coronary artery disease, then the absence of coronary artery disease can be determined.

[0102] Although the window function used in the above embodiments starts from the earliest temporally sampled point in the point sequence, those skilled in the art will understand that it can also start from the latest temporally sampled point in the point sequence.

[0103] In the above embodiments, if two sampling points are determined to meet the preset conditions during the traversal process, but the first high-frequency QRS complex data (or the first high-frequency QRS waveform curve) may show significant fluctuations between these two sampling points, the cause of this situation may be coronary artery spasm or other factors such as microcirculatory disturbance. To eliminate such interference factors, in the preferred embodiment of this application, after determining the first and second sampling points that meet the preset conditions, the first-order difference of the RMS voltage / amplitude / intensity of the sampling points between the first and second sampling points is calculated to determine the third sampling point (minimum point) corresponding to the minimum value and at least one fourth sampling point (maximum point) corresponding to the maximum value. Here, the minimum point refers to the point where the first-order difference result changes from negative to positive, and the maximum point refers to the point where the first-order difference result changes from positive to negative. For any fourth sampling point among at least one fourth sampling point, calculate the absolute value of the RMS voltage difference between the fourth sampling point and the third sampling point (referred to as the first RMS voltage difference), and calculate the RMS voltage difference between the first sampling point and the second sampling point (referred to as the second RMS voltage difference). If the ratio of the first RMS voltage difference to the second RMS voltage difference is greater than or equal to a third preset threshold, it is determined that there is a significant fluctuation amplitude. Therefore, it is determined that there are no waveform characteristics indicative of coronary artery lesions between these two sampling points (the first sampling point and the second sampling point) in the first high-frequency QRS complex data (it is considered that the first and second sampling points do not meet the preset conditions). The process continues to iterate according to the case where the preset conditions are not met as described in the above embodiment. If the ratio is less than the third preset threshold, the influence of significant fluctuation amplitude can be excluded, and the first and second sampling points meet the preset conditions, indicating the presence of a steep drop wave. When there are multiple fourth sampling points, it is preferable that the ratio obtained for each fourth sampling point is less than the third preset threshold, thus excluding the influence of significant fluctuation amplitude. In one example, the third preset threshold can be, for example, 40%, 50%, etc. Using this method to eliminate the interference of significant fluctuation amplitude in determining the presence of steep drop waves can further improve the specificity of the assessment.

[0104] Compared to the methods for determining the presence of coronary artery stenosis disclosed in prior patent applications, the coronary artery lesion determination method provided in the above embodiments can significantly improve sensitivity without significantly reducing or even reducing specificity.

[0105] In a further embodiment of this application, the type of coronary artery lesion can be further determined, i.e., coronary artery stenosis or coronary artery sclerosis. In this embodiment, the method may further include the following steps.

[0106] In step S1035, the positive index in the first lead corresponding to the first high-frequency QRS complex data with waveform characteristics indicating coronary artery lesions is determined. The method for determining the positive index in the first lead is as described above and will not be repeated here.

[0107] In step S1035, if the positive indicator in the first lead is positive, the type of coronary artery lesion is determined to be coronary artery stenosis. Specifically, if a borderline situation (i.e., a state between positive and negative) is considered, a positive indicator in the first lead includes both positive and borderline situations.

[0108] In step S1036, if the positive indicator in the first lead is negative, the type of coronary artery lesion is determined to be coronary sclerosis.

[0109] In this embodiment of the application, during the assessment of vascular response capability, if it is determined that the first high-frequency QRS complex data corresponding to any electrocardiogram lead has a first waveform feature, it can be determined that there is a risk of coronary artery disease, and the risk level of the coronary artery disease can be further assessed, which is denoted as the first risk level S1.

[0110] It is possible to determine the relative decrease in amplitude and the absolute decrease in voltage between the maximum and minimum root mean square voltage values ​​within a preset time period in the first high-frequency QRS complex data corresponding to each ECG lead. For example, the preset time period may include a period before exercise, a period during exercise, and a period after exercise. The period before exercise is located in the resting phase, the period during exercise includes the entire exercise phase, and the period after exercise is located in the recovery phase. The periods before exercise, the period during exercise, and the period after exercise are sequentially consecutive time periods. Taking the time range of 3 to 9 minutes corresponding to the exercise phase in the first high-frequency QRS complex data as an example, the preset time period may be represented by the time interval [1 minute 20 seconds, 9 minutes 20 seconds]. It starts at time point 1 minute 20 seconds and ends at time point 9 minutes 20 seconds. This preset time period includes 100 seconds before exercise, 6 minutes during exercise, and 20 seconds after exercise.

[0111] In one example, the maximum value of the RMS voltage occurs earlier than the minimum value. The unit for the relative decrease in amplitude can be, for example, a percentage (%), and the unit for the absolute decrease in voltage can be, for example, uV (microvolts).

[0112] The maximum relative amplitude decrease and the maximum absolute voltage decrease among the relative amplitude decrease and absolute voltage decrease values ​​corresponding to each electrocardiogram lead are determined as the target maximum relative amplitude decrease and the target maximum absolute voltage decrease values.

[0113] In this embodiment of the application, the value of the first risk level can be determined based on the relative decrease in the target maximum amplitude and the absolute decrease in the target maximum voltage.

[0114] For example, risk levels can be categorized based on the relative decrease in the target maximum amplitude and the absolute decrease in the target maximum voltage, with different risk levels corresponding to different risk values. In this example, the risk value can be normalized to obtain a value in the interval [0,1].

[0115] For example, if it is determined that the first waveform feature, such as a steep drop wave, does not exist, then the value of the first risk level S1 can be 0, i.e., S1=0, indicating no risk. If the first waveform feature exists, the relative amplitude drop threshold can be divided into multiple threshold intervals, or the absolute voltage drop threshold can be divided into multiple threshold intervals. Each threshold interval corresponds to a first risk level value. After determining the target maximum relative amplitude drop value and the target maximum absolute voltage drop value, the corresponding first risk level value is determined based on their respective threshold intervals. In one example, if the first risk level value corresponding to the target maximum relative amplitude drop value is different from the first risk level value corresponding to the target maximum absolute voltage drop value, then the larger of the two values ​​can be taken.

[0116] If the relative decrease in the target maximum amplitude is in the range of [0%, 35%), or the absolute decrease in the target maximum voltage is in the range of [0, 1) (unit uV), then the value of the first risk level S1 can be 0.1, i.e., S1 = 0.1.

[0117] If the relative decrease in the target maximum amplitude is in the range of [35%, 50%), or the absolute decrease in the target maximum voltage is in the range of [1, 2) (unit uV), then the value of the first risk level S1 can be 0.3, i.e., S1 = 0.3.

[0118] If the relative decrease in the target maximum amplitude is in the range of [50%, 65%), or the absolute decrease in the target maximum voltage is in the range of [2, 3) (unit uV), then the value of the first risk level S1 can be 0.6, i.e., S1 = 0.6.

[0119] If the relative decrease in the target maximum amplitude is greater than or equal to 65%, or the absolute decrease in the target maximum voltage is greater than or equal to 3 (unit uV), then the value of the first risk level S1 can be 0.9, i.e., S1=0.9.

[0120] Those skilled in the art will understand that the above division of the intervals for the relative decrease in maximum amplitude and the absolute decrease in maximum voltage, as well as the assignment of the first risk level, are exemplary, and other interval division methods and assignments can be adopted according to actual applications and needs.

[0121] In this embodiment, the risk of myocardial bridging can be identified or determined based on the waveform type of the first high-frequency QRS complex data. This waveform type may include waveform features (second waveform features) that indicate myocardial bridging, such as a slow-falling wave. The likelihood (risk) of myocardial bridging can be determined by judging whether the first high-frequency QRS complex data has second waveform features.

[0122] The following describes in detail the method for determining or identifying whether a second waveform feature (e.g., a drooping wave) exists in the second high-frequency QRS group data.

[0123] Figure 6 An example flowchart illustrating a method according to an embodiment of this application for determining whether a second waveform feature exists in first high-frequency QRS complex data (to determine the likelihood of myocardial bridging). Figure 6 As shown, specifically in this embodiment, the method may include the following steps.

[0124] In step S1041, candidate first high-frequency QRS complex data are determined from the first high-frequency QRS complex data corresponding to each ECG lead. These candidate first high-frequency QRS complex data do not exhibit waveform characteristics (first waveform characteristics) indicative of coronary artery lesions, such as steep descent waves. When analyzing the first high-frequency QRS complex data to determine the presence of myocardial bridging, first high-frequency QRS complex data exhibiting waveform characteristics indicative of coronary artery lesions can be excluded first. That is, if the first high-frequency QRS complex data exhibits waveform characteristics indicative of coronary artery lesions, such as steep descent waves, then this first high-frequency QRS complex data is no longer used to determine the presence of myocardial bridging. The method for determining whether the first high-frequency QRS complex data exhibits waveform characteristics indicative of coronary artery lesions is as described in the above embodiment and will not be repeated here.

[0125] In step S1042, a point sequence (which can be called a first high-frequency QRS time-intensity point sequence) of each candidate first high-frequency QRS group data is obtained. This point sequence may include multiple sampling points arranged in time sequence. These multiple sampling points may be multiple data points sampled or selected from a set of multiple data points of the candidate first high-frequency QRS group data at a set sampling period (sampling interval) or sampling frequency.

[0126] In step S1043, for any candidate first high-frequency QRS group data point sequence, any one of the multiple sampling points in the point sequence is used as a reference sampling point, and the sampling points within a preset time interval starting from the reference sampling point are traversed to determine whether there is an earlier first sampling point and a later second sampling point that meet the preset conditions within the preset time interval.

[0127] In one example scenario, the preset condition may be that the relative value of the amplitude decrease between the first and second sampling points reaches a fourth preset threshold and the degree of voltage decrease between the first and second sampling points reaches a fifth preset threshold, and the root mean square voltage of each sampling point between the first and second sampling points shows a continuous decreasing trend (hereinafter referred to as the third preset condition). In another example scenario, the preset condition may be that the relative value of the amplitude decrease between the first and second sampling points reaches a fourth preset threshold or the degree of voltage decrease between the first and second sampling points reaches a fifth preset threshold, and the root mean square voltage of each sampling point between the first and second sampling points shows a continuous decreasing trend (hereinafter referred to as the fourth preset condition).

[0128] The relative amplitude decrease can be, for example, expressed as: (RMS voltage at the first sampling point - RMS voltage at the second sampling point) / RMS voltage at the first sampling point. In one example, the voltage decrease can be expressed as the absolute voltage decrease, which is the RMS voltage at the first sampling point minus (-) the RMS voltage at the second sampling point. In an alternative example, the voltage decrease can be expressed as the ratio of the absolute voltage decrease to the maximum voltage value, i.e., (RMS voltage at the first sampling point - RMS voltage at the second sampling point) / maximum voltage value. This maximum voltage value can be understood as the maximum power, which can be used to reflect the subject's maximum cardiac pumping function, i.e., the peak value of the candidate first high-frequency QRS complex data will not exceed this maximum voltage value. The maximum voltage value is determined based on the maximum value of the RMS voltage in the high-frequency QRS complex data of all ECG leads. In this example, the fifth preset threshold can be expressed as a percentage, just like the fourth preset threshold.

[0129] In step S1044, the possibility of myocardial bridging is determined if preset conditions are met. During the traversal, if a first sampling point and a second sampling point that meet the preset conditions appear, it indicates that the candidate first high-frequency QRS complex data has waveform characteristics indicative of myocardial bridging, such as a slow-falling wave. In this case, the possibility of myocardial bridging can be determined. Here, the slow-falling wave has a gentler downward trend in the displayed waveform compared to the steep-falling wave.

[0130] In step S1045, if the preset conditions are not met, step S1043 is repeated after changing the reference sampling point. In this step, if no first or second sampling point meeting the preset conditions appears after all sampling points within the preset time interval have been traversed, the current traversal loop ends. At this time, the reference sampling point can be changed, and the next traversal loop can be entered. For example, the adjacent sampling points of the current reference sampling point can be used as the new reference sampling point. The sampling points within the preset time interval starting from the new reference sampling point can be traversed using the new reference sampling point as the reference to determine whether there is a first or second sampling point meeting the preset conditions. If there is a first or second sampling point meeting the preset conditions, it indicates that the candidate first high-frequency QRS complex data line has waveform characteristics indicating myocardial bridging, and the traversal ends. If no first or second sampling point meeting the preset conditions appears after the current traversal loop ends, the next traversal loop is entered until a first or second sampling point meeting the preset conditions appears, or all sampling points in the point sequence that need to be traversed have been traversed. If the preset conditions are still not met after all the sampling points to be traversed, then it is determined that there are no waveform features indicating myocardial bridging.

[0131] There are multiple ways or rules for traversing the sampling points. Figure 7 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to an embodiment of this application is shown. Figure 7As shown, in this embodiment, for each candidate first high-frequency QRS complex data (i.e., the waveform characteristics of the complex data that do not indicate coronary artery lesions), the earliest sampling point in the time sequence can be used as the reference sampling point (which can be called the initial reference point). The next sampling point in the time sequence of the initial reference point is used as the traversal starting point, and the traversal is performed according to the time sequence. The relative value of amplitude decrease and the degree of voltage decrease between the initial reference point (the first sampling point in this embodiment) and the current traversed sampling point (the second sampling point in this embodiment) are calculated. It is determined whether the initial reference point and the current traversed sampling point meet the preset conditions. If these two sampling points meet the preset conditions, it can be determined that the candidate first high-frequency QRS complex data has waveform characteristics indicating myocardial bridging, such as a slow-falling wave, thereby determining the possibility of myocardial bridging, and the traversal stops. If the preset conditions are not met, it is determined whether the time interval between the current traversed sampling point and the initial reference point exceeds the preset duration, and whether the current traversed sampling point is the last sampling point in the point sequence. If the time interval between the current traversed sampling point and the initial reference point does not exceed a preset duration and the current traversed sampling point is not the last sampling point, then switch to the next sampling point of the current traversed sampling point, and use this next sampling point as the new current traversed sampling point. The same operation is then performed on this new current traversed sampling point. Specifically, the relative value of amplitude decrease and the degree of voltage decrease between the initial reference point and the new current traversed sampling point are calculated. It is then determined whether the initial reference point and the new current traversed sampling point meet preset conditions. If the preset conditions are met, it can be determined that the candidate first high-frequency QRS complex data has a slow-falling wave, thus confirming the possibility of myocardial bridging, and the traversal stops. If the preset conditions are not met, the above operation can be repeated to traverse subsequent sampling points. If the preset conditions are still not met, traversal can continue until the time interval between the traversed current traversed sampling point and the initial reference point reaches a preset duration (in this embodiment, the preset duration is the aforementioned preset time interval), or the current traversed sampling point is the last sampling point in the point sequence. If the time interval between the current traversed sampling point and the initial reference point reaches a preset duration, or if the current traversed sampling point is the last sampling point in the point sequence, then the process can switch to the next sampling point after the initial reference point, using this next sampling point as the reference sampling point, and proceeding with the next traversal loop in the same manner as described above. If the preset conditions are still not met, the traversal loop can continue until the preset conditions are met, or until all sampling points in the point sequence except the last sampling point have been used as reference sampling points to complete the traversal. In other words, if the preset conditions are still not met after the traversal is completed using the previous sampling point as the reference sampling point, it indicates that there are no waveform characteristics indicative of myocardial bridging, such as slow-falling waves, for the candidate first high-frequency QRS complex data.

[0132] In this embodiment, the preset duration can range from, for example, 4 to 6 minutes. If the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold and the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the third preset condition), then the fourth preset threshold can range from 30% to 40%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the fifth preset threshold can range from 1 uV to 2 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the fifth preset threshold can range from 10% to 20%.

[0133] If the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold, or the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the fourth preset condition), then the value of the fourth preset threshold can range from 50% to 60%. In this case, the value of the fifth preset threshold is related to the preset duration. Generally speaking, the value of the fifth preset threshold is positively correlated with the preset duration; that is, the longer the preset duration, the larger the fifth preset threshold. For example, taking a preset duration range of 4 to 6 minutes as an example, if the preset duration is 4 minutes, then when the voltage decrease is the absolute value of the voltage decrease, the value of the fifth preset threshold ranges from 1 uV to 2 uV; when the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, the value of the fifth preset threshold ranges from 10% to 20%. If the preset duration is 5 minutes, then when the voltage drop is the absolute value of the voltage drop, the fifth preset threshold ranges from 2 uV to 3 uV; when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the second preset threshold ranges from 20% to 30%. If the preset duration is 6 minutes, when the voltage drop is the absolute value of the voltage drop, the fifth preset threshold ranges from 3 uV to 4 uV; when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the second preset threshold ranges from 30% to 40%. Those skilled in the art will understand that the specific values ​​of the above preset thresholds are exemplary and are intended to illustrate the correlation between the fifth preset threshold and the preset duration. For a smaller preset duration, using a larger fifth preset threshold may misidentify a sloping wave as a non-sloping wave. For a larger preset duration, using a smaller fifth preset threshold may misidentify a non-sloping wave (such as a flat wave) as a sloping wave, thereby reducing the recognition accuracy. Similarly, the preset duration and the fourth preset threshold are also positively correlated to further improve the recognition accuracy. The specific values ​​can be adjusted according to the actual application.

[0134] The above processing can be applied to all candidate first high-frequency QRS complex data. If at least one candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, such as a slow-falling wave, then the possibility of myocardial bridging can be determined. Conversely, if none of the candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, then the absence of myocardial bridging can be determined.

[0135] Although the above embodiments process multiple sampling points in a point sequence in a time sequence from early to late, those skilled in the art will understand that the above method is also applicable to multiple sampling points in a time sequence from late to early. In this case, the relative value of amplitude decrease and the degree of voltage decrease between the currently traversed sampling point and the reference sampling point are calculated (in this case, the currently traversed sampling point is earlier than the reference sampling point in time).

[0136] Figure 8 An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to another embodiment of this application is shown. Figure 8As shown, in this embodiment, a window function can be preset. The window of this window function includes a start point and an end point, and the distance between the start point and the end point is the window length (in this embodiment, the window length is the preset time interval mentioned above). The value range of the window length can be, for example, 4 minutes to 6 minutes. For each candidate first high-frequency QRS complex data, the window function is used to traverse the point sequence of the candidate first high-frequency QRS complex data. Specifically, the earliest sampling point in the time sequence can be used as the starting point of the window. The sampling point with the largest RMS voltage (the first sampling point in this embodiment) and the sampling point with the smallest RMS voltage (the second sampling point in this embodiment) among the multiple sampling points in the point sequence included in the window can be determined. The relative value of the amplitude decrease and the degree of voltage decrease between the first sampling point and the second sampling point are calculated. It is determined whether the first sampling point and the second sampling point meet the preset conditions. If these two sampling points meet the preset conditions, it can be determined that the candidate first high-frequency QRS complex data has a slow-falling wave, and the possibility of myocardial bridging can be determined, and the traversal stops. If the preset conditions are not met, the window of the window function can be slid backward by a preset step size, and the above operation can be repeated. If the preset conditions are still not met, the window can be slid backward by the preset step size again until the preset conditions are met, or until the end of the window reaches or exceeds the last sampling point in the point sequence. That is, if the preset conditions are not met even after the window has slid forward by a preset step size and its end has reached or exceeded the last sampling point, it indicates that there are no waveform features indicative of myocardial bridging, such as a slow-falling wave, for the candidate first high-frequency QRS complex data. In this embodiment, the preset step size can be, for example, an integer multiple of the time interval (e.g., sampling period) between two adjacent sampling points, preferably, the time interval between two adjacent sampling points, such as 10 seconds. Of course, those skilled in the art will understand that setting the preset step size to other values ​​is also feasible.

[0137] In this embodiment, if the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold and the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the third preset condition), then the value range of the fourth preset threshold can be 30% to 40%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 1 uV to 2 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 10% to 20%.

[0138] If the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold, or the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the fourth preset condition), then the value of the fourth preset threshold can range from 50% to 60%. In this case, the value of the fifth preset threshold is related to the window length. Generally speaking, the value of the fifth preset threshold is positively correlated with the window length; that is, the larger the window length, the larger the fifth preset threshold. For example, taking a window length ranging from 4 minutes to 6 minutes as an example, if the window length is 4 minutes, then when the voltage decrease is the absolute value of the voltage decrease, the value of the fifth preset threshold ranges from 1 uV to 2 uV; when the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, the value of the fifth preset threshold ranges from 10% to 20%. If the window length is 5 minutes, the fifth preset threshold ranges from 2 uV to 3 uV when the voltage drop is the absolute value of the voltage drop, and the second preset threshold ranges from 20% to 30% when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value. If the window length is 6 minutes, the fifth preset threshold ranges from 3 uV to 4 uV when the voltage drop is the absolute value of the voltage drop, and the second preset threshold ranges from 30% to 40% when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value. Those skilled in the art will understand that the specific values ​​of the above preset thresholds are exemplary and are intended to illustrate the correlation between the fifth preset threshold and the window length. For a smaller window length, using a larger fifth preset threshold may misidentify a sloping wave as a non-sloping wave. For a larger window length, using a smaller fifth preset threshold may misidentify a non-sloping wave (such as a flat wave) as a sloping wave, thereby reducing the recognition accuracy. Similarly, the window length is also positively correlated with the fourth preset threshold in order to further improve the recognition accuracy. The specific values ​​can be adjusted according to the actual application.

[0139] The above processing can be performed on all candidate first high-frequency QRS complex data. If at least one candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, such as a slow-falling wave, then the possibility of myocardial bridging can be determined. Conversely, if none of the candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, then the absence of myocardial bridging can be determined.

[0140] Although the window function used in the above embodiments starts from the earliest temporally sampled point in the point sequence, those skilled in the art will understand that it can also start from the latest temporally sampled point in the point sequence.

[0141] Figure 9An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to another embodiment of this application is shown. Figure 9 As shown, in this embodiment, a window function can be preset. The window of this window function includes a start point and an end point, and the distance between the start point and the end point is the window length (in this embodiment, the window length is the preset time interval mentioned above). The value range of the window length can be, for example, 4 minutes to 6 minutes. For each candidate first high-frequency QRS complex data, the start point of the window can be aligned with the earliest sampling point in the point sequence in terms of time (i.e., these two points coincide in time). Using this first sampling point as a reference point, each sampling point within the window is traversed sequentially. Specifically, the relative value of amplitude decrease and the degree of voltage decrease between the reference point and the currently traversed sampling point within the window can be calculated. It is determined whether the reference point and the currently traversed sampling point meet preset conditions. If these two sampling points meet the preset conditions, it can be determined that the candidate first high-frequency QRS complex data has waveform characteristics indicating myocardial bridging, such as a slow-falling wave, and the possibility of myocardial bridging can be determined, and the traversal stops. If the preset conditions are not met, the next sampling point within the window is traversed. If the reference point and the currently traversed sampling point meet the preset conditions, the traversal stops; otherwise, it continues until the last sampling point within the traversal window. If the preset conditions are still not met after all sampling points within the window have been traversed, the traversal loop ends, and the window of the window function can be slid backward (time-wise backward) by a preset step size, and the above operation (the next traversal loop) is repeated. If no situation meets the preset conditions after the traversal loop ends, the window can be slid backward by the preset step size again until the preset conditions are met, or until the end of the window reaches or exceeds the last sampling point in the point sequence. That is, if no situation meets the preset conditions after the window has slid by a preset step size and its end reaches or exceeds the last sampling point, it indicates that there are no waveform features indicative of myocardial bridging, such as a slow-falling wave, for the candidate first high-frequency QRS complex data. In this embodiment, the preset step size can be, for example, an integer multiple of the time interval (e.g., sampling period) between two adjacent sampling points, preferably, the time interval between two adjacent sampling points, such as 10 seconds. Of course, those skilled in the art will understand that setting the preset step size to other values ​​is also feasible.

[0142] In this embodiment, if the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold and the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the third preset condition), then the value range of the fourth preset threshold can be 30% to 40%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 1 uV to 2 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 10% to 20%.

[0143] If the preset conditions include the relative amplitude decrease between the first and second sampling points reaching a fourth preset threshold, or the voltage decrease between the first and second sampling points reaching a fifth preset threshold (the fourth preset condition), then the value of the fourth preset threshold can range from 50% to 60%. In this case, the value of the fifth preset threshold is related to the window length. Generally speaking, the value of the fifth preset threshold is positively correlated with the window length; that is, the larger the window length, the larger the fifth preset threshold. For example, taking a window length ranging from 4 minutes to 6 minutes as an example, if the window length is 4 minutes, then when the voltage decrease is the absolute value of the voltage decrease, the value of the fifth preset threshold ranges from 1 uV to 2 uV; when the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, the value of the fifth preset threshold ranges from 10% to 20%. If the window length is 5 minutes, the fifth preset threshold ranges from 2 uV to 3 uV when the voltage drop is the absolute value of the voltage drop, and the second preset threshold ranges from 20% to 30% when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value. If the window length is 6 minutes, the fifth preset threshold ranges from 3 uV to 4 uV when the voltage drop is the absolute value of the voltage drop, and the second preset threshold ranges from 30% to 40% when the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value. Those skilled in the art will understand that the specific values ​​of the above preset thresholds are exemplary and are intended to illustrate the correlation between the fifth preset threshold and the window length. For a smaller window length, using a larger fifth preset threshold may misidentify a sloping wave as a non-sloping wave. For a larger window length, using a smaller fifth preset threshold may misidentify a non-sloping wave (such as a flat wave) as a sloping wave, thereby reducing the recognition accuracy. Similarly, the window length is also positively correlated with the fourth preset threshold in order to further improve the recognition accuracy. The specific values ​​can be adjusted according to the actual application.

[0144] The above processing can be performed on all candidate first high-frequency QRS complex data. If at least one candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, such as a slow-falling wave, then the possibility of myocardial bridging can be determined. Conversely, if none of the candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, then the absence of myocardial bridging can be determined.

[0145] Although the window function used in the above embodiments starts from the earliest temporally sampled point in the point sequence, those skilled in the art will understand that it can also start from the latest temporally sampled point in the point sequence.

[0146] In the above embodiments, the preset time interval (e.g., preset duration, window length) for myocardial bridging determination is larger than the preset time interval (e.g., preset duration, window length) for coronary artery lesion determination; the fourth preset threshold is greater than or equal to the first preset threshold; and the fifth preset threshold is greater than or equal to the second preset threshold. Furthermore, the time interval between two adjacent sampling points in the multiple sampling points for myocardial bridging determination can be larger than the time interval between two adjacent sampling points in the multiple sampling points for coronary artery lesion determination. For example, the time interval between two adjacent sampling points in the multiple sampling points for myocardial bridging determination can be, for example, 12 seconds, and the time interval between two adjacent sampling points in the multiple sampling points for coronary artery lesion determination can be, for example, 10 seconds.

[0147] In a preferred embodiment of this application, in order to improve the accuracy of myocardial bridging, after obtaining the candidate first high-frequency QRS complex data, the candidate first high-frequency QRS complex data can be smoothed to smooth out the points with slight fluctuations. For example, the root mean square voltage / intensity / amplitude corresponding to multiple data points can be smoothed according to the time sequence to obtain the smoothed candidate first high-frequency QRS complex data.

[0148] In a preferred embodiment of this application, the continuous decreasing trend of the root mean square voltage at each sampling point between the first and second sampling points in the preset condition can be determined in the following way. Adding this condition to the judgment can further improve the accuracy of myocardial bridging judgment or identification.

[0149] Specifically, if it is determined that there exist first and second sampling points in the candidate first high-frequency QRS group data that satisfy the following conditions, namely: The relative decrease in amplitude between the first and second sampling points reaches the fourth preset threshold, and the decrease in voltage between the first and second sampling points reaches the fifth preset threshold (the case of the third preset condition); or The relative decrease in amplitude between the first and second sampling points reaches a fourth preset threshold, or the decrease in voltage between the first and second sampling points reaches a fifth preset threshold (under the fourth preset condition). The first-order difference of the RMS voltage / amplitude / intensity of multiple sampling points within the current preset time interval (e.g., preset duration, window length) of the candidate first high-frequency QRS complex data can be calculated to obtain a difference sequence. The number of consecutive values ​​greater than or equal to zero in this difference sequence is determined. If this number is less than or equal to a threshold value, such as 2 (i.e., flat waves or rising waves can be excluded), then it can be determined that the root mean square voltage of each sampling point between the first and second sampling points shows a continuous decreasing trend. At this time, it can be determined that the candidate first high-frequency QRS complex data has waveform characteristics indicative of myocardial bridging, such as a slow-falling wave, and the possibility of myocardial bridging can be determined.

[0150] In a preferred embodiment of this application, to further improve the accuracy of myocardial bridging judgment or identification, for example, to further rule out the possibility of steeply drooping waves in the candidate first high-frequency QRS complex data, after obtaining the differential sequence, all differential sequence groups are determined. Each differential sequence group includes a consecutive number of differential values, and the sum of that number of differential values ​​in each sequence group is calculated. For example, assuming the differential sequence includes N differential values, the number of consecutive differential values ​​is n, where N and n are natural numbers, and n < N. Then the first sequence group may include the first differential value to the nth differential value, the second sequence group may include the second differential value to the (n+1)th differential value, and so on. If the number of consecutive values ​​greater than or equal to zero in the differential sequence is less than or equal to 2, and the sum of the differential values ​​in each differential sequence group is greater than or equal to a predetermined value, it can be determined that the candidate first high-frequency QRS complex data has waveform characteristics indicative of myocardial bridging, such as a slow drooping wave, and the possibility of myocardial bridging can be determined. The sum of multiple consecutive difference values ​​can further eliminate steep descent conditions, i.e., exclude cases where steep descent waves exist in the candidate first high-frequency QRS group data. In one example, the number of consecutive difference values ​​for each sequence group can be, for example, 5, and the predetermined value can be, for example, -15. Of course, those skilled in the art will understand that as long as the purpose of eliminating steep descent waves is achieved, other values ​​can be set according to the actual application.

[0151] Theoretically, the root mean square voltage drop in the first high-frequency QRS complex data caused by myocardial bridging will persist for a period of time (e.g., 20 seconds) after the end of exercise and last for at least 3 minutes, and the drop will be gradual. In alternative or additional embodiments of this application, a technical solution can be designed based on this phenomenon to determine the possibility of myocardial bridging.

[0152] Figure 10An example flowchart illustrating a method for determining the likelihood of the presence of myocardial bridging according to another embodiment of this application is shown. Figure 10 As shown, in this embodiment, candidate first high-frequency QRS complex data can be determined from the first high-frequency QRS complex data corresponding to each electrocardiogram lead. The candidate first high-frequency QRS complex data does not have waveform characteristics that indicate coronary artery lesions, such as steep descent waves.

[0153] For any candidate first high-frequency QRS complex data, at least one sampling point within a preset time period is determined as a reference sampling point. In one example, the preset time period may be the time interval from the end of exercise to a point after the end of exercise during exercise ECG detection, for example, from the end of exercise to 20 to 30 seconds after the end of exercise. In this embodiment, in step S1043, the reference sampling point is limited to the sampling points determined within a period of time after the end of exercise.

[0154] The sampling points to be traversed are determined based on the established reference sampling point. These sampling points are those within a preset time interval preceding the reference sampling point in the candidate first high-frequency QRS complex data. In one example, this preset time interval is located within the exercise phase of the stress-induced ECG detection process. The preset time interval can include multiple consecutive sub-intervals (i.e., adjacent sub-intervals are adjacent but do not overlap). For example, if the reference sampling point is the end point of the exercise and the time of that end point is 9 minutes (starting from the beginning of the stress-induced ECG detection process), then the preset time interval can be 3 to 6 minutes preceding the reference sampling point. The preset time interval can include a first sub-interval, a second sub-interval, and a third sub-interval. The first sub-interval can be 3 to 4 minutes (excluding 3 minutes but including 4 minutes) before the reference sampling point. Taking the start of the stress exercise ECG detection process as the zero point, the first sub-interval can be represented as [5 minutes, 6 minutes). The second sub-interval can be 4 to 5 minutes before the reference sampling point, and can also be represented as [4 minutes, 5 minutes). The third sub-interval can be 5 to 6 minutes before the reference sampling point, and can also be represented as [3 minutes, 4 minutes]. In this embodiment, if the number of at least one reference sampling point includes multiple points, each reference sampling point can have a corresponding preset time interval. That is, different timestamps of reference sampling points result in different timestamps of the corresponding preset time intervals, and the timestamps of each sub-interval within the preset time interval can also be different.

[0155] The sampling points within multiple sub-intervals can be traversed to determine whether there are target sampling points in each sub-interval that satisfy the preset conditions corresponding to each sub-interval. In this embodiment, the preset conditions corresponding to different sub-intervals can be different.

[0156] In one example scenario, the preset condition could be that the root mean square voltage of each sampling point between the target sampling point and the reference sampling point shows a continuous decreasing trend, and the relative value of the amplitude decrease between the target sampling point and the reference sampling point reaches a fourth preset threshold, and the degree of voltage decrease between the target sampling point and the reference sampling point reaches a fifth preset threshold (the third preset condition). In another example scenario, the preset condition could be that the root mean square voltage of each sampling point between the target sampling point and the reference sampling point shows a continuous decreasing trend, and the relative value of the amplitude decrease between the target sampling point and the reference sampling point reaches a fourth preset threshold, or the degree of voltage decrease between the target sampling point and the reference sampling point reaches a fifth preset threshold (the fourth preset condition).

[0157] In this embodiment, the preset conditions corresponding to different sub-intervals can be different. Specifically, the fourth preset threshold and the fifth preset threshold corresponding to different sub-intervals can be different. The further the sub-interval is from the reference sampling point in time, the larger the fourth preset threshold and the fifth preset threshold are.

[0158] For example, if the preset conditions include the relative amplitude decrease between the target sampling point and the reference sampling point reaching a fourth preset threshold and the voltage decrease between the target sampling point and the reference sampling point reaching a fifth preset threshold (the third preset condition), then for the first sub-interval, the value range of the fourth preset threshold can be 30% to 40%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 2 uV to 3 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 30% to 40%. For the second sub-interval, the value range of the fourth preset threshold can be 40% to 50%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 3 uV to 4 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 40% to 50%. For the third sub-interval, the value range of the fourth preset threshold can be 50% to 60%. In this case, if the voltage drop is the absolute value of the voltage drop, the fifth preset threshold can range from 4 uV to 5 uV. If the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the fifth preset threshold can range from 50% to 60%.

[0159] If the preset conditions include the relative amplitude decrease between the target sampling point and the reference sampling point reaching a fourth preset threshold and the voltage decrease between the target sampling point and the reference sampling point reaching a fifth preset threshold (the third preset condition), then for the first sub-interval, the value range of the fourth preset threshold can be 40% to 50%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 3 uV to 4 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 40% to 50%. For the second sub-interval, the value range of the fourth preset threshold can be 50% to 60%. In this case, if the voltage decrease is the absolute value of the voltage decrease, then the value range of the fifth preset threshold can be 4 uV to 5 uV. If the voltage decrease is the ratio of the absolute value of the voltage decrease to the maximum voltage value, then the value range of the fifth preset threshold can be 50% to 60%. For the third sub-interval, the value range of the fourth preset threshold can be 60% to 70%. In this case, if the voltage drop is the absolute value of the voltage drop, the fifth preset threshold can range from 5 uV to 6 uV. If the voltage drop is the ratio of the absolute value of the voltage drop to the maximum voltage value, the fifth preset threshold can range from 60% to 70%.

[0160] During the traversal, if any sub-interval among multiple sub-intervals contains a target sampling point that meets the corresponding preset conditions, the possibility of myocardial bridging can be determined. The traversal method can include traversing the sampling points in reverse temporal order, or performing parallel traversal across multiple sub-intervals. For cases where at least one reference sampling point includes multiple reference sampling points, if no target reference point meeting the preset conditions is found after completing all traversals for a certain reference sampling point, the traversal operation is restarted with the next reference sampling point. If no target reference point meeting the preset conditions is found after traversing all reference sampling points, it can be determined that the candidate first high-frequency QRS complex data does not contain waveform features indicative of myocardial bridging.

[0161] The above processing can be applied to all candidate first high-frequency QRS complex data. If at least one candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, such as a slow-falling wave, then the possibility of myocardial bridging can be determined. Conversely, if none of the candidate first high-frequency QRS complex data shows waveform characteristics indicative of myocardial bridging, then the absence of myocardial bridging can be determined.

[0162] The method for determining that the root mean square voltage of each sampling point between the target sampling point and the reference sampling point in the preset conditions in this embodiment shows a continuous downward trend can be the same as that in the previous embodiments, and will not be repeated here.

[0163] In this embodiment of the application, during the assessment of vascular response capability, if it is determined that the first high-frequency QRS complex data corresponding to any electrocardiogram lead has a second waveform feature, then the risk of myocardial bridging can be determined, and the risk level of the myocardial bridging risk can be further assessed, denoted as the second risk level S2.

[0164] If there is no risk of myocardial bridging, the value of S2 for the second risk level can be 0, i.e., S2=0, indicating no risk.

[0165] If there is a risk of myocardial bridging, the value of the second risk level can be determined based on the number of ECG leads corresponding to positive results in the first lead (which can be called the number of first positive leads).

[0166] For example, the risk level can be classified based on the number of first positive leads, and different risk levels can correspond to different risk values. In the example, the risk value can be normalized, for example, to obtain a value in the interval [0,1].

[0167] Taking a 12-lead ECG test as an example, if the number of positive leads is less than or equal to (≤) 3, it indicates mild ischemia, and the value of the second risk level S2 can be 0.3, i.e., S2=0.3; if the number of positive leads is greater than or equal to 4 and less than or equal to 6 (i.e., in the [4,6] interval), the value of the second risk level S2 can be 0.6; if the number of positive leads is greater than or equal to (≥) 7, it indicates severe ischemia, and the value of the second risk level S2 can be 0.9, i.e., S2=0.9.

[0168] Those skilled in the art will understand that the above division of the number of first positive leads and the assignment of the second risk level are exemplary, and other division methods and assignments can be adopted according to actual applications and needs.

[0169] In this embodiment of the application, the (target) first high-frequency QRS complex data that has neither the first waveform feature nor the second waveform feature in the first high-frequency QRS complex data corresponding to each electrocardiogram lead can be used to identify or determine the risk of coronary microcirculation disorder.

[0170] The methods for diagnosing or determining the presence of coronary microcirculation disorders will be described in detail below.

[0171] Figure 11 An example flowchart illustrating a method for determining the likelihood of coronary microcirculatory disturbance according to an embodiment of this application is shown. Figure 11 As shown, specifically in this embodiment, the method may include the following steps.

[0172] The first high-frequency QRS complex data can be obtained from exercise ECG data, and the second high-frequency QRS complex data can be obtained from resting ECG data.

[0173] In step S1051, the first high-frequency QRS complex data indicating positive lead positive indicators are identified among all the first high-frequency QRS complex data.

[0174] In step S1052, it is determined whether a target first high-frequency QRS complex exists in the positive first high-frequency QRS complex data indicating positive leads. This target first high-frequency QRS complex data does not contain waveform features indicative of coronary artery lesions (here referred to as the first waveform feature) and / or waveform features indicative of myocardial bridging (here referred to as the second waveform feature). When analyzing the first high-frequency QRS complex data to determine whether there is coronary microcirculatory disturbance, the first high-frequency QRS complex data containing the first waveform feature (e.g., steep descent wave) and / or the second waveform feature (e.g., slow descent wave) can be excluded first. That is, if the first high-frequency QRS complex data contains the first waveform feature and / or the second waveform feature, then the first high-frequency QRS complex data is no longer used to determine whether there is coronary microcirculatory disturbance. The methods for determining whether the first high-frequency QRS complex data contains waveform features indicative of coronary artery lesions and the methods for determining whether the first high-frequency QRS complex data contains waveform features indicative of myocardial bridging are as described in the above embodiments and will not be repeated here.

[0175] In step S1053, if the existence of target first high-frequency QRS complex data is determined, the possibility of coronary microcirculation disorder is determined.

[0176] To further improve the accuracy of determining the possibility of coronary microcirculatory disturbances, it is possible to further determine whether the lead positive indicators of the target second high-frequency QRS complex data corresponding to the target ECG lead associated with the target first high-frequency QRS complex data indicate a positive result.

[0177] Specifically, once a target first high-frequency QRS complex data point is identified, the target ECG lead associated with that data point can be determined; that is, the target first high-frequency QRS complex data is output through that target ECG lead. The target second high-frequency QRS complex data output through that target ECG lead can then be analyzed to determine whether the lead-positive indicators of the target second high-frequency QRS complex data indicate a positive result.

[0178] If the lead indicator of the target second high-frequency QRS complex data is positive, the possibility of coronary microcirculatory disturbance is determined. For example, if the target ECG lead corresponding to the target first high-frequency QRS complex data is lead V1, and the lead indicator of the corresponding second high-frequency QRS complex data in lead V1 at rest is positive, then the possibility of coronary microcirculatory disturbance is determined.

[0179] In this embodiment of the application, step S105 may further include determining that there is no coronary microcirculation obstruction. Specifically, in step S1054, a first maximum RMS voltage is determined based on the maximum value of the RMS voltage in the first high-frequency QRS complex data corresponding to all electrocardiogram leads, and a second maximum voltage is determined based on the maximum value of the voltage in the second high-frequency QRS complex data corresponding to all electrocardiogram leads.

[0180] In one example, the first maximum RMS voltage can be the maximum voltage value in the above embodiments. For a single exercise load test, the first maximum RMS voltage can be obtained by rounding up to an even number of the maximum RMS voltage values ​​in the first high-frequency QRS complex data corresponding to all ECG leads. In another example, the first maximum RMS voltage can also be obtained by taking the maximum RMS voltage values ​​in the first high-frequency QRS complex data corresponding to all ECG leads.

[0181] Similarly, the second maximum voltage can be the voltage value obtained by rounding up to an even number from the maximum voltage / amplitude values ​​of the second high-frequency QRS complex data (or the second high-frequency QRS envelope curve) corresponding to all ECG leads. In another example, the maximum voltage value in the second high-frequency QRS complex data corresponding to all ECG leads can also be used as the second maximum voltage.

[0182] In step S1055, the number of positive leads in at least one electrocardiogram lead is determined based on the second high-frequency QRS complex data, and the positive leads correspond to the positive second QRS complex data indicated by the positive index of the second lead. In step S1056, the number of positive leads is compared with a preset value, the first maximum RMS voltage is compared with a first voltage threshold, and the second maximum voltage is compared with a second voltage threshold. In step S1057, if the number of positive leads is less than a preset value and at least the first maximum RMS voltage is greater than a first voltage threshold, it indicates that the subject has strong cardiopulmonary function and it can be determined that there is no coronary microcirculation obstruction. The condition that at least the first maximum RMS voltage is greater than the first voltage threshold can include the first maximum RMS voltage being greater than the first voltage threshold and the second maximum voltage being greater than the second voltage threshold, or the first maximum RMS voltage being greater than the first voltage threshold but the second maximum voltage being less than or equal to the second voltage threshold. In one example, the preset value could be, for example, 3, the first voltage threshold could be, for example, 8 uV, and the second voltage threshold could be, for example, 14 uV. However, those skilled in the art will understand that other preset values ​​and voltage thresholds can be set according to the actual application.

[0183] In this embodiment of the application, during the assessment of vascular responsiveness, if the possibility (risk) of coronary microcirculation disorder is determined, the risk level of this coronary microcirculation disorder can be further assessed, denoted as the third risk level S3. In the example, the risk level value can be normalized, for example, to obtain a value in the interval [0,1].

[0184] In one example, the value of the third risk level S3 can be determined based on the positive index of the second lead in the target second high-frequency QRS complex data. For example, if the positive index of the second lead indicates a positive result, the value of the third risk level S3 can be determined to be 0.9, denoted as S3=0.9; if the positive index of the second lead indicates a negative result, the value of the third risk level S3 can be determined to be 0.6, denoted as S3=0.6.

[0185] In another example, the value of the third risk level S3 can be determined based on the cardiopulmonary function assessment index. For example, if the cardiopulmonary function index is greater than or equal to an index threshold, such as 0.7, indicating strong cardiopulmonary function, the value of the third risk level S3 can be determined to be 0.3, denoted as S3=0.3.

[0186] In another example, the value of the third risk level S3 can be determined based on the maximum exercise voltage (e.g., the first maximum voltage mentioned above). For example, if the maximum exercise voltage is greater than (>) 8 uV, indicating strong cardiopulmonary function, the value of the third risk level S3 can be determined to be 0.3, denoted as S3=0.3. In an alternative example, the value of the third risk level S3 can be determined based on both the maximum exercise voltage and the maximum resting voltage (e.g., the second maximum voltage mentioned above). For example, if the maximum exercise voltage is greater than (>) 8 uV, or the maximum resting voltage is greater than (>) 14 uV, indicating strong cardiopulmonary function, the value of the third risk level S3 can be determined to be 0.3, denoted as S3=0.3. In this example, if the results of the judgment based on these two conditions are inconsistent, the judgment based on the maximum exercise voltage shall prevail.

[0187] In one example, if it is determined that cardiopulmonary function is strong and the number of ECG leads corresponding to positive results in the second lead (referred to as the number of second positive leads) is less than a threshold (e.g., 3), then the value of the third risk level S3 can be determined to be 0, denoted as S3=0.

[0188] Those skilled in the art will understand that the various thresholds and the assignment of the third risk level mentioned above are exemplary, and other thresholds and assignments can be adopted according to actual applications and needs.

[0189] In this embodiment, the risk level of heart failure can be determined based on the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration for each electrocardiogram lead, denoted as the fourth risk level S4. In the example, the risk level value can be normalized, for example, to obtain a value in the interval [0,1].

[0190] In one embodiment, the value of the fourth risk level S4 can be determined based on the number of heart failure features appearing among the following heart failure characteristics. Heart failure characteristics may include, but are not limited to: (1) QRS duration > a time limit threshold, for example, 140ms; (2) There are multiple peaks and the amplitude voltage is low. For example, there is at least one ECG lead whose second high-frequency QRS complex data has a peak number greater than or equal to a peak number threshold (e.g., 4) and an amplitude voltage less than a voltage threshold (e.g., 4 uV). The amplitude voltage can be the maximum voltage (peak voltage) in the second high-frequency QRS complex data.

[0191] (3) The number of second positive leads is greater than or equal to a number threshold (for example, the number threshold is 6 when using 12 ECG leads). (4) The number of ECG leads corresponding to a high frequency morphology index greater than or equal to a high frequency morphology index threshold (e.g., 30%) is greater than or equal to a number threshold (e.g., the number threshold is 3 for a 12-lead ECG).

[0192] If any one of the above four heart failure characteristics is present, then the value of the fourth risk level S4 can be determined to be 0.3, i.e., S4 = 0.3; If two of the above four heart failure characteristics are present, then the value of the fourth risk level S4 can be determined to be 0.6, i.e., S4 = 0.6; If any one of the above three heart failure characteristics is present, then the value of the fourth risk level S4 can be determined to be 0.9, i.e., S4 = 0.9. Alternatively, risk scores can be assigned to each of the above heart failure characteristics, and then the value of the fourth risk level S4 can be determined based on the ratio of the sum of the risk scores of the heart failure characteristics to the sum of the risk scores of the above heart failure characteristics.

[0193] For example, the risk scores corresponding to the heart failure features (1) to (4) above can be 3, 2, 2, and 1 respectively, with a total score of 8. If the heart failure features (1), (3), and (4) above are present, the sum of the risk scores is 6, and the final determined value of the fourth risk level S4 is S4 = 6 / 8 = 0.75. In one example, in order to keep the measurement size consistent with other risk levels, this value can be multiplied by a scaling factor. For example, if the maximum value of other risk levels (S1, S2, S3) is 0.9, then after multiplying by the scaling factor, the value of the fourth risk level S4 can be S4 = 6 / 8 * 0.9 = 0.675.

[0194] After determining the values ​​for each risk level, vascular responsiveness can be assessed to obtain a vascular responsiveness index.

[0195] Specifically, in one embodiment, the most severe single risk factor can be highlighted, conforming to the clinical principle of "prioritizing critical risks" (e.g., severe coronary artery stenosis has a far greater impact on vascular function than minor myocardial bridging). The vascular responsiveness index can be negatively correlated with the largest of the first, second, third, and fourth risk levels, as illustrated by the expression: V2_index = 1-max(S1,S2,S3,S4), where V2_index is the vascular responsiveness index. This approach provides intuitive results, quickly identifies life-threatening risks, and is suitable for emergency departments and high-risk population screening (where rapid identification of core risks is required).

[0196] In another embodiment, the impact of different risk levels on the vascular responsiveness index can be comprehensively considered. For example, weights can be assigned to the first, second, third, and fourth risk levels, denoted as w1, w2, w3, and w4, respectively. The values ​​of w1, w2, w3, and w4 can be, for example, 0.4, 0.1, 0.2, and 0.3. The vascular responsiveness index can be negatively correlated with the weighted sum of the first, second, third, and fourth risk levels, as illustrated by the expression: V2_index = 1 - (w1×S1 + w2×S2 + w3×S3 + w4×S4). In this approach, factors with higher clinical importance and risk (such as coronary artery stenosis) should have higher weights to highlight their impact on overall risk, comprehensively considering the real-world scenario of multiple risks coexisting (such as the common combination of vascular disease and heart failure in clinical practice). The weights can be validated through clinical data, resulting in more accurate results, and are suitable for routine physical examinations and chronic disease management (which require a comprehensive assessment of overall risk).

[0197] For example, coronary artery stenosis (S1) ≥ heart failure (S4) ≥ coronary microcirculatory disturbance (S3) > myocardial bridging (S2) (severe coronary artery stenosis and heart failure are fatal risks, while myocardial bridging is mostly benign). When using weighted summation, the weights reflect the clinical severity and prevalence of each risk. Example weight allocation is as follows: Coronary artery stenosis risk (S1): Weight range: 0.3 - 0.5, optimal value: 0.4, reason: it is the main factor leading to acute cardiovascular events (such as myocardial infarction) and has the greatest harm.

[0198] Myocardial bridging risk (S2): Weight range: 0.05 - 0.2, Optimal value: 0.1, Reason: Most are benign, only cause symptoms under specific circumstances, high-risk cases need attention, overall risk is relatively low, and weight is the lowest.

[0199] Risk of coronary microcirculatory disturbance (S3): Weight range: 0.1 - 0.30, optimal value: 0.20, rationale: common in non-obstructive coronary artery disease, non-fatal but affecting prognosis, its importance is becoming increasingly prominent.

[0200] Heart failure risk (S4): Weight range: 0.2 - 0.4, optimal value: 0.30, reason: it is the end stage of many heart diseases, with high risk, and heart failure has a significant impact on vascular response and overall cardiac function.

[0201] In step S108, myocardial cell viability is assessed based on the number of peaks, high-frequency morphology index, and positive indicators of the second lead for each electrocardiogram lead to obtain the myocardial cell viability index.

[0202] Specifically, in this embodiment of the application, the first sub-index value H1 can be determined based on the high-frequency morphology index. In the example, the first sub-index value can be normalized, for example, to obtain a value in the interval [0,1].

[0203] For example, the High Frequency Morphology Index (HFMI): HFMI is in the range [0, 5%), H1 = 0; If the subject's age is <50 years: HFMI in [5%, 15%), H1=0.1; HFMI in [15%, 25%), H1=0.3; HFMI in [25%, 35%), H1=0.5; HFMI in [35%, 45%), H1=0.7; HFMI ≥45%, H1=0.9; If the subject's age is ≥50 years: HFMI in [5%, 8%), H1=0.1; HFMI in [8%, 25%), H1=0.3; HFMI in [25%, 35%), H1=0.5; HFMI in [35%, 45%), H1=0.7; HFMI ≥45%, H1=0.9.

[0204] In this embodiment of the application, the second sub-index value R1 can be determined based on the number of second positive leads. In the example, the second sub-index value can be normalized, for example, to obtain a value in the interval [0,1].

[0205] For example, the number of second positive leads: 0, R1=0; located in [1,3], R1=0.3; located in [4,6], R1=0.5; located in [7,9], R1=0.6; located in [10,12], R1=0.9.

[0206] In this embodiment of the application, the third sub-index value H2 can be determined based on the number of peaks. In the example, the third sub-index value can be normalized, for example, to obtain a value in the interval [0,1].

[0207] For example, the maximum number of peaks among all ECG leads: single peak (peaks = 1), H2 = 0; double peak (peaks = 2), H2 = 0.3; triple peak (peaks = 3), H2 = 0.7; (peaks ≥ 4), or, if a flat wave appears, H2 = 0.9.

[0208] In this embodiment, a flat wave can refer to a significant decrease in the amplitude and a flat shape of the second high-frequency QRS envelope curve. A specific quantification standard can be, for example, that for any ECG lead, compared with the baseline voltage (specifically, the average voltage of the isoelectric baseline segment corresponding to the high-frequency QRS complex in that ECG lead), if the percentage of sampling points in the second high-frequency QRS complex data (or the second high-frequency QRS envelope curve) of that ECG lead with a voltage fluctuation amplitude (difference between the sampling point voltage and the baseline voltage) ≤ a voltage value (e.g., 1 μV) is greater than or equal to a preset threshold (e.g., 95% or 100%), meaning the second high-frequency QRS envelope curve exhibits a horizontal or near-horizontal trend, then it is determined that the high-frequency QRS complex data corresponding to that lead contains a flat wave, which is also determined to be the second high-frequency QRS envelope curve corresponding to that lead containing a flat wave.

[0209] The cardiomyocyte viability index V3_index can be determined based on the determined values ​​of each sub-index. In one example, the cardiomyocyte viability index V3_index can be negatively correlated with the weighted sum of the values ​​of each sub-index. An example expression could be: 1-(a1×H1+ a2×R1+ a3×H2), where a1, a2, and a3 are the weights of the first, second, and third sub-index values, respectively, for example, 0.4, 0.3, and 0.3.

[0210] Weights reflect the direct correlation between each sub-indicator and the electrophysiological stability and degree of damage to cardiomyocytes. An example weight allocation is shown below: High-frequency morphology index (H1): Weight range: 0.3 - 0.50, preferably 0.4, because changes in high-frequency components can sensitively reflect local conduction disorders and ischemia in the myocardium and are directly related to myocardial cell vitality.

[0211] Number of positive leads at rest (R1): Weighting range: 0.25 - 0.40, preferably 0.30. Reason: It reflects the spatial range of myocardial injury or electrical excitation. The wider the range, the worse the myocardial vitality and the higher the risk.

[0212] Maximum number of peaks (H2): Weight range: 0.20 - 0.35, preferably 0.3. Reason: Multiple peaks or flat waves reflect that the overall function of the heart is affected. The more peaks or the appearance of flat waves, the weaker the overall function of the heart and the greater the possibility of heart failure.

[0213] In step S109, the cardiac health assessment results are determined based on the cardiopulmonary function index, vascular responsiveness index, and myocardial cell viability index.

[0214] Specifically, in one example, the cardiac health assessment result can be obtained by multiplying the cardiopulmonary function index, vascular responsiveness index, and cardiomyocyte vitality index, i.e., V = V1_index * V2_index * V3_index, where V is the cardiac health assessment result. Using the product of three-dimensional indices can highlight the weakest link effect.

[0215] In one embodiment of this application, the cardiac health assessment results can be corrected based on clinical information to obtain a corrected cardiac health assessment result.

[0216] For example, basic questioning factors about the test subjects can be incorporated: K1 (Past Medical History): No relevant medical history, K1=1.0; 1 item (e.g., hypertension), K1=0.9; 2 items (e.g., hypertension + diabetes), K1=0.8; ≥3 items or past myocardial infarction / stroke, K1=0.7.

[0217] K2 (medication): If there is a history of taking vasodilators, K2=0.9; if not, K2=1.0.

[0218] K3 (gender and menstrual period): For non-menstrual females / males, K3=1.0; for menstrual females, K2=1.1.

[0219] K4 (detection time): Morning (e.g., before 12 o'clock), K4=1.0; Afternoon (e.g., after 12 o'clock), K4=1.05.

[0220] The revised cardiac health assessment result V_final can be expressed as: V_final = V × K1 × K2 × K3 × K4.

[0221] Heart health status can be determined based on the revised heart health assessment results, for example: 0.6≤V_final indicates a healthy condition; 0.4≤V_final<0.6 indicates a mild abnormality; 0.2≤V_final<0.4 indicates a moderate abnormality; and V_final<0.2 indicates a severe abnormality.

[0222] In the embodiments of this application, the weights of each indicator and sub-indicator in cardiopulmonary function, vascular response capacity, and cardiomyocyte viability can be set according to: 1) the clinical predictive value of the indicator (such as the correlation with the endpoint event); 2) the stability of the indicator (signal-to-noise ratio); 3) expert consensus (e.g., the Delphi method).

[0223] In this application embodiment, a processor is provided, configured to execute the methods described in the above embodiments.

[0224] In this application embodiment, a machine-readable storage medium is provided, on which instructions are stored, which are used to cause a machine to perform the cardiac health status assessment method described in the above embodiments.

[0225] In this application embodiment, a processing device is provided, including a processor and a memory, wherein instructions are stored in the memory, and the processor is configured to call and execute the instructions from the memory to implement the cardiac health status assessment method described in the above embodiments.

[0226] Examples of processors may include, but are not limited to, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).

[0227] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0228] Figure 12 An example block diagram of a cardiac health status assessment system according to an embodiment of this application is illustrated schematically. Figure 12 As shown in the embodiments of this application, a cardiac health status assessment system is provided, which may include: An electrocardiogram (ECG) signal acquisition device 100 includes at least one electrode forming at least one ECG lead for acquiring ECG signals from a subject; and The aforementioned processing device 200.

[0229] The ECG signal acquisition device 100 may also include a display configured to display various data and information, including but not limited to, subject information such as age, height, weight, etc., and ECG signals / QRS complex data / QRS waveform curves acquired through ECG leads.

[0230] The processing device 200 can be configured to perform the cardiac health status assessment method described in the above embodiments.

[0231] Existing cardiac health assessment methods are mostly single-dimensional and fail to construct a holistic assessment system around the core cardiac physiological chain of "oxygen intake - oxygen transport - oxygen utilization," resulting in low assessment accuracy. The technical solution provided in this application takes oxygen transport efficiency as the core and establishes a three-dimensional closed-loop assessment system of "oxygen intake (cardiopulmonary function) - oxygen transport (vascular response) - oxygen utilization (myocardial cell vitality)," which fully matches the core chain of cardiac physiological function, provides comprehensive assessment dimensions, solves the deficiency of "single-dimensional assessment" in existing technologies, and meets the clinical need for "holistic assessment" of cardiac health.

[0232] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0233] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0234] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0235] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0236] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0237] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0238] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0239] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0240] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for assessing cardiac health status, characterized in that, include: Acquire both exercise ECG data and resting ECG data output from at least one ECG lead; The first high-frequency QRS complex data were obtained based on the exercise electrocardiogram data; The first maximum voltage and the waveform category and positive index of the first high-frequency QRS complex corresponding to each ECG lead are obtained based on the first high-frequency QRS complex data of all ECG leads. The second high-frequency QRS complex data were obtained based on the resting electrocardiogram data; The second maximum voltage, the number of peaks, high-frequency morphology index, positive index of the second lead, and QRS duration of each electrocardiogram lead are obtained from the second high-frequency QRS complex data corresponding to all electrocardiogram leads. Cardiopulmonary function is assessed based on the first maximum voltage and / or the second maximum voltage to obtain a cardiopulmonary function index; Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive index of second lead and QRS duration of each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, wherein the risk of heart disease includes at least one of coronary artery disease risk, myocardial bridging risk and coronary microcirculation disorder risk. Myocardial cell viability was assessed based on the number of peaks, high-frequency morphology index, and positive indicators in the second lead of each electrocardiogram lead to obtain the myocardial cell viability index; and The cardiac health assessment results were determined based on the cardiopulmonary function index, vascular responsiveness index, and myocardial cell vitality index. Where the heart disease risk includes the risk of coronary artery disease, determining the presence of coronary artery disease risk based on waveform category includes: The first point sequence corresponding to each ECG lead is obtained based on the first high-frequency QRS complex data corresponding to each ECG lead. The first point sequence includes multiple sampling points arranged in time sequence. For any ECG lead corresponding to the first point sequence, in one iteration, any one of the multiple sampling points in the first point sequence is taken as the current reference sampling point, and the sampling points within the first preset time interval starting from the current reference sampling point are traversed to determine whether there are first sampling points and second sampling points that meet preset conditions within the first preset time interval, wherein the first sampling point is earlier than the second sampling point in time. If it is determined that there are first sampling points and second sampling points that meet the preset conditions, the first waveform feature of the first high-frequency QRS complex data corresponding to the arbitrary electrocardiogram lead is determined to indicate coronary artery lesions. If it is determined that there is no first sampling point or second sampling point that meets the preset conditions, the current traversal loop ends and other sampling points in the first point sequence other than the current reference sampling point are determined as new reference sampling points. For each new reference sampling point, the next traversal loop is performed until it is determined that there is a first sampling point and a second sampling point that meet the preset conditions, or all the sampling points that need to be traversed in the first point sequence have been traversed.

2. The method for assessing cardiac health status according to claim 1, characterized in that, Cardiopulmonary function is assessed based on the first and second maximum voltages to obtain a cardiopulmonary function index, including: The first maximum voltage and the second maximum voltage are normalized to obtain the first value and the second value, respectively. Assign a first weight and a second weight to the first value and the second value respectively, wherein the first weight is greater than the second weight; The first and second values ​​are weighted and summed using the first and second weights to obtain the cardiopulmonary function index.

3. The method for assessing cardiac health status according to claim 1, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, including: In cases where the risk of coronary artery disease is determined based on the waveform category, the relative decrease in amplitude and the absolute decrease in voltage between the maximum and minimum root mean square voltage values ​​within a preset time period are determined in the first high-frequency QRS complex data corresponding to each ECG lead. The preset time period includes a period before the exercise phase, the exercise phase, and a period after the exercise phase during the stress exercise ECG detection process, and the maximum root mean square voltage value is earlier than the minimum root mean square voltage value. The maximum relative amplitude decrease and the maximum absolute voltage decrease among the relative amplitude decrease and absolute voltage decrease values ​​corresponding to each electrocardiogram lead are determined as the target maximum relative amplitude decrease and the target maximum absolute voltage decrease values. The first risk level of coronary artery disease risk is determined based on the relative decrease in the target maximum amplitude and the absolute decrease in the target maximum voltage.

4. The method for assessing cardiac health status according to claim 1, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, including: If the risk of myocardial bridging is determined based on the waveform category, the second risk level of myocardial bridging is determined based on the number of ECG leads corresponding to positive results indicated by the positive index in the first lead.

5. The method for assessing cardiac health status according to claim 1, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, including: In cases where the risk of coronary microcirculation disorder is determined based on waveform category, the third risk level of the risk of coronary microcirculation disorder is determined based on the positive index of the second lead of the target second high-frequency QRS complex data; wherein, the electrocardiogram lead corresponding to the target second high-frequency QRS complex data corresponds to the target first high-frequency QRS complex data, the positive index of the first lead of the target first high-frequency QRS complex data indicates positivity, and there are no first waveform features indicating coronary artery lesions or second waveform features indicating myocardial bridging.

6. The method for assessing cardiac health status according to claim 5, characterized in that, If the second lead of the target second high-frequency QRS complex data indicates a positive result, the third risk level will be determined as the first value. If the second lead of the target second high-frequency QRS complex data indicates a negative result, the third risk level is determined as the second value, where the second value is less than the first value.

7. The method for assessing cardiac health status according to claim 6, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, which also includes: If no coronary microcirculatory disturbance is determined based on the waveform category, the third risk level is determined as the third value if the first maximum voltage is greater than or equal to the voltage threshold or if the cardiopulmonary function index is greater than or equal to the index threshold, wherein the third value is less than the second value, and the first maximum voltage is determined based on the maximum value of the root mean square voltage in the first high-frequency QRS complex data corresponding to all ECG leads.

8. The method for assessing cardiac health status according to claim 7, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, which also includes: If the first maximum voltage is greater than or equal to the voltage threshold, and the number of ECG leads corresponding to positive results in the second lead is less than a threshold, the third risk level is determined as the fourth value, where the fourth value is less than the third value.

9. The method for assessing cardiac health status according to claim 1, characterized in that, Based on the risk of heart disease determined by waveform type and the risk of heart failure determined by the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead, vascular responsiveness is assessed to obtain a vascular responsiveness index, including: The risk of heart failure is determined based on the number of peaks, high-frequency morphology index, positive indicators in lead 2, and QRS duration for each electrocardiogram lead. The determination of heart failure risk based on the number of peaks in each electrocardiogram lead, high-frequency morphology index, positive indicators in lead 2, and QRS duration includes: The fourth risk level of heart failure is determined based on the presence of a specific heart failure feature and / or the number of heart failure features present in a plurality of heart failure features, including: QRS duration is greater than the time limit threshold; The number of peaks in the second high-frequency QRS complex data corresponding to at least one ECG lead is greater than or equal to a peak number threshold, and the amplitude voltage is less than a voltage threshold. The positive index of the second lead indicates that the number of positive electrocardiogram leads is greater than or equal to the first threshold. The number of ECG leads corresponding to a high-frequency morphology index greater than or equal to the high-frequency morphology index threshold is greater than or equal to the second quantity threshold.

10. The method for assessing cardiac health status according to any one of claims 3 to 9, characterized in that, The vascular responsiveness index is negatively correlated with the largest of the first, second, third, and fourth risk levels, or negatively correlated with the weighted sum of the first, second, third, and fourth risk levels.

11. The method for assessing cardiac health status according to claim 1, characterized in that, Cardiac cell viability is assessed based on the number of peaks, high-frequency morphology index, and positive indicators in the second lead of each electrocardiogram lead to obtain a cardiomyocyte viability index, including: The value of the first sub-indicator is determined based on the high-frequency morphological index; The value of the second sub-index is determined based on the number of ECG leads corresponding to a positive result indicated by the positive index of the second lead. The third sub-index value is determined based on the maximum number of peaks among all electrocardiogram leads. The cardiomyocyte vitality index is determined based on the first sub-index value, the second sub-index value, and the third sub-index value. The cardiomyocyte vitality index is negatively correlated with the result obtained by weighted summation of the first sub-index value, the second sub-index value, and the third sub-index value.

12. The method for assessing cardiac health status according to claim 1, characterized in that, Also includes: The cardiac health assessment results were revised based on the baseline questionnaire factors of the test subjects to obtain the revised cardiac health assessment results. The baseline questionnaire factors included at least one of the following: Past medical history; History of taking vasodilators; Gender and menstrual cycle; Detection time.

13. The method for assessing cardiac health status according to claim 1, characterized in that, Determining the risk of coronary artery disease based on waveform type also includes: If, after all the sampling points that need to be traversed in the first point sequence have been traversed, there are no first sampling points or second sampling points that meet the preset conditions, it is determined that the corresponding first high-frequency QRS complex data does not contain the first waveform feature indicating coronary artery lesions. If the first high-frequency QRS complex data corresponding to all electrocardiogram leads do not show the first waveform characteristics that indicate coronary artery disease, then the risk of coronary artery disease is determined to be absent.

14. The method for assessing cardiac health status according to claim 1, characterized in that, The preset conditions include: The relative decrease in amplitude between the first sampling point and the second sampling point reaches a first preset threshold, and the degree of voltage decrease between the first sampling point and the second sampling point reaches a second preset threshold; or The relative value of the amplitude decrease between the first sampling point and the second sampling point reaches the first preset threshold, or the degree of voltage decrease between the first sampling point and the second sampling point reaches the second preset threshold.

15. The method for assessing cardiac health status according to claim 4, characterized in that, Based on waveform type, the risk of myocardial bridging includes: Candidate first high-frequency QRS complex data are determined from the first high-frequency QRS complex data corresponding to each electrocardiogram lead. The candidate first high-frequency QRS complex data does not have the first waveform characteristics that indicate coronary artery lesions. The corresponding second point sequence is obtained based on the candidate first high-frequency QRS group data. The second point sequence includes multiple sampling points arranged in time sequence. For any candidate first high-frequency QRS group data corresponding to the second point sequence, in one traversal loop, any one of the multiple sampling points in the second point sequence is taken as the current reference sampling point, and the sampling points in the second preset time interval starting from the current reference sampling point are traversed to determine whether there are a third sampling point and a fourth sampling point that satisfy another preset condition in the second preset time interval, wherein the third sampling point is earlier than the fourth sampling point in time. If it is determined that there are third and fourth sampling points that meet another preset condition, the second waveform feature indicating myocardial bridging is determined to exist in the arbitrary candidate first high-frequency QRS complex data. If it is determined that there are no third or fourth sampling points that satisfy another preset condition, the current traversal loop ends, and other sampling points in the second point sequence other than the current reference sampling point are determined as new reference sampling points; For the new reference sampling point, perform the next traversal loop until it is determined that there are a third and fourth sampling points that satisfy another preset condition, or all the sampling points that need to be traversed in the second point sequence have been traversed.

16. The method for assessing cardiac health status according to claim 15, characterized in that, Determining the risk of myocardial bridging based on waveform type also includes: If, after all the sampling points that need to be traversed in the second point sequence have been traversed, there are no third and fourth sampling points that meet another preset condition, it is determined that the corresponding candidate first high-frequency QRS complex data does not have the second waveform feature indicating myocardial bridging. If no second waveform feature indicating myocardial bridging is found in all candidate first high-frequency QRS complex data, then myocardial bridging is determined to be absent.

17. The method for assessing cardiac health status according to claim 15, characterized in that, The other preset condition includes: The relative decrease in amplitude between the third and fourth sampling points reaches the third preset threshold, and the degree of voltage decrease between the third and fourth sampling points reaches the fourth preset threshold, and the root mean square voltage of each sampling point between the third and fourth sampling points shows a continuous decreasing trend; or The relative value of the amplitude decrease between the third and fourth sampling points reaches the third preset threshold or the degree of voltage decrease between the third and fourth sampling points reaches the fourth preset threshold, and the root mean square voltage of each sampling point between the third and fourth sampling points shows a continuous downward trend.

18. The method for assessing cardiac health status according to claim 5, characterized in that, Based on waveform type, the risk of coronary microcirculation disturbance includes: Determine whether there is a target first high-frequency QRS complex data in the first high-frequency QRS complex data corresponding to the positive indicator of the first lead, and whether the target first high-frequency QRS complex data does not have the first waveform feature and the second waveform feature. Given the existence of the target first high-frequency QRS complex data, a risk of coronary microcirculatory disturbance is identified.

19. A processing apparatus, characterized in that, The device includes a processor and a memory, the memory storing instructions, and the processor is configured to call and execute the instructions from the memory to implement the cardiac health status assessment method according to any one of claims 1 to 18.

20. A cardiac health status assessment system, characterized in that, include: An electrocardiogram (ECG) signal acquisition device includes at least one electrode forming at least one ECG lead for acquiring the ECG signal of a subject. as well as The processing apparatus according to claim 19.

21. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the cardiac health assessment method according to any one of claims 1 to 18.

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