Myocardial injury risk assessment method and device, computer device and storage medium

Abstract

The application relates to a myocardial injury risk assessment method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining and analyzing a resting electrocardio signal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardio signal; analyzing the high-frequency QRS envelope curve to obtain resting high-frequency QRS characteristics including a resting positive lead number, a resting critical lead number, a first target lead number, a second target lead number, a third target lead number and a high-frequency morphology index; the first target lead number refers to the number of leads with a wave peak number greater than or equal to a first number threshold; the second target lead number refers to the number of leads with a wave peak number equal to a second number threshold; the third target lead number refers to the number of leads with a wave peak number equal to a third number threshold; analyzing the resting low-frequency electrocardio signal to obtain old myocardial infarction information and other resting low-frequency characteristics; and analyzing a myocardial injury risk assessment characteristic set including the resting high-frequency QRS characteristics and the resting low-frequency characteristics to obtain a myocardial injury risk assessment score.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a myocardial injury risk assessment method and device, computer equipment and storage medium. BACKGROUND

[0002] Myocardial injury refers to the damage of myocardial cells caused by various reasons, common reasons including but not limited to infection of bacteria, viruses and the like, oxygen supply and demand imbalance, heart causes, systemic diseases and the like. Myocardial injury can have various effects on the body, and severe myocardial injury can also cause heart failure, cardiogenic shock and the like. Therefore, timely diagnosis and treatment are needed to improve the myocardial injury state, so as to avoid serious damage.

[0003] At present, in addition to evaluating myocardial injury based on relevant biomarkers (such as cardiac troponin), myocardial injury is usually evaluated based on changes in low-frequency components such as ST segment elevation and T wave inversion in electrocardiogram, but not all myocardial injuries can be reflected in the low-frequency components of electrocardiogram, thus there is a problem of low evaluation accuracy. SUMMARY

[0004] Therefore, it is necessary to provide a myocardial injury assessment method, device, computer equipment, storage medium and computer program product capable of improving the accuracy of myocardial injury assessment.

[0005] A myocardial injury risk assessment method, the method comprising:

[0006] obtaining a resting electrocardiogram signal;

[0007] analyzing the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardiogram signal;

[0008] performing feature analysis on the high-frequency QRS envelope curve to obtain a resting high-frequency QRS feature; the resting high-frequency QRS feature includes the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, and the high-frequency morphology index of each lead; the number of first target leads refers to the number of leads with a peak number greater than or equal to a first number threshold; the number of second target leads refers to the number of leads with a peak number equal to a second number threshold; the number of third target leads refers to the number of leads with a peak number equal to a third number threshold;

[0009] performing feature analysis on the resting low-frequency electrocardiogram signal to obtain a resting low-frequency feature; the resting low-frequency feature includes old myocardial infarction information;

[0010] The risk assessment score of myocardial injury is obtained by performing evaluation analysis on the risk assessment feature set of myocardial injury; the risk assessment feature set of myocardial injury comprises the high-frequency QRS feature in rest and the low-frequency feature in rest.

[0011] In one of the embodiments, the high-frequency QRS feature in rest further comprises a fourth target lead number and a fifth target lead number; the fourth target lead number is the number of chest leads whose lead positive indicators are positive; and the fifth target lead number is the number of limb leads whose lead positive indicators are positive.

[0012] In one of the embodiments, the risk assessment feature set of myocardial injury further comprises a target risk assessment score of myocardial ischemia; and the method further comprises:

[0013] obtaining a historical risk assessment score of myocardial ischemia and / or a current risk assessment score of myocardial ischemia; the historical risk assessment score of myocardial ischemia is obtained by analyzing historical detection data in a first time period; the current risk assessment score of myocardial ischemia is obtained by analyzing current detection data in a second time period; and the first time period is earlier than the second time period;

[0014] obtaining a target risk assessment score of myocardial ischemia according to the historical risk assessment score of myocardial ischemia and / or the current risk assessment score of myocardial ischemia.

[0015] In one of the embodiments, obtaining the current risk assessment score of myocardial ischemia comprises:

[0016] obtaining a motion electrocardio signal collected in the second time period;

[0017] analyzing the motion electrocardio signal to obtain a high-frequency QRS envelope curve;

[0018] performing feature analysis on the high-frequency QRS waveform curve to obtain a motion high-frequency QRS feature; the motion high-frequency QRS feature comprises a number of motion positive leads and a number of motion critical leads;

[0019] obtaining a current risk assessment score of myocardial ischemia by performing evaluation analysis on a risk assessment feature set of myocardial ischemia; the risk assessment feature set of myocardial ischemia comprises the motion high-frequency QRS feature.

[0020] In one of the embodiments, the risk assessment feature set of myocardial ischemia further comprises a motion low-frequency feature; and the obtaining step of the motion low-frequency feature comprises:

[0021] extracting a motion low-frequency electrocardio signal from the motion electrocardio signal;

[0022] performing feature analysis on the motion low-frequency electrocardiosignal to obtain a motion low-frequency feature; the motion low-frequency feature includes a change degree of an ST-T segment of each lead and a change duration of the ST-T segment.

[0023] In one of the embodiments, the myocardial injury risk assessment feature set further includes user basic information; the method further includes:

[0024] obtaining user basic information; the user basic information includes at least one of chronic basic disease information, alcohol consumption information, and staying up late information.

[0025] In one of the embodiments, the evaluation analysis on the myocardial injury risk assessment feature set to obtain a myocardial injury risk assessment score includes:

[0026] inputting each feature in the myocardial injury risk assessment feature set into a corresponding feature function to obtain a corresponding myocardial injury risk assessment sub-score, and summing up each myocardial injury risk assessment sub-score to obtain a myocardial injury risk assessment score;

[0027] The pre-configuration of the feature function corresponding to each feature in the myocardial injury risk assessment feature set includes:

[0028] segmenting a value range corresponding to the feature to obtain one or more numerical ranges;

[0029] determining a feature sub-function corresponding to each numerical range according to a correlation between the feature and the myocardial injury risk and a weight of the feature;

[0030] obtaining the corresponding pre-configured feature function according to each feature sub-function corresponding to the feature.

[0031] A myocardial injury risk assessment device, the device includes:

[0032] an acquisition module configured to acquire a resting electrocardiosignal;

[0033] an analysis module configured to analyze the resting electrocardiosignal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardiosignal;

[0034] The analysis module is further configured to perform feature analysis on the high-frequency QRS envelope curve to obtain a resting high-frequency QRS feature; the resting high-frequency QRS feature includes a number of resting positive leads, a number of resting critical leads, a first target lead number, a second target lead number, a third target lead number, and a high-frequency morphology index of each lead; the first target lead number refers to a number of leads with a wave peak number greater than or equal to a first number threshold; the second target lead number refers to a number of leads with a wave peak number equal to a second number threshold; and the third target lead number refers to a number of leads with a wave peak number equal to a third number threshold.

[0035] The analysis module is further configured to perform feature analysis on the resting low-frequency electrocardio signal to obtain resting low-frequency features; and the resting low-frequency features include old myocardial infarction information.

[0036] The evaluation module is configured to perform evaluation analysis on a set of myocardial injury risk evaluation features to obtain a myocardial injury risk evaluation score; and the set of myocardial injury risk evaluation features includes the resting high-frequency QRS features and the resting low-frequency features.

[0037] In one of the embodiments, a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method described in each method embodiment when executing the computer program.

[0038] In one of the embodiments, a computer readable storage medium stores a computer program, and the computer program implements the steps of the method described in each method embodiment when executed by a processor.

[0039] In one of the embodiments, a computer program product includes a computer program, and the computer program implements the steps of the method described in each method embodiment when executed by a processor.

[0040] The myocardial injury evaluation method, device, computer device, storage medium, and computer program product described above obtain high-frequency QRS envelope curves and resting low-frequency electrocardio signals by acquiring and analyzing resting electrocardio signals, analyze the resting high-frequency QRS features including the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, and the high-frequency morphology index of each lead based on the high-frequency QRS envelope curves, and analyze the resting low-frequency features including old myocardial infarction information based on the resting low-frequency electrocardio signals, so as to comprehensively evaluate the myocardial injury risk by combining the resting high-frequency QRS features and the resting low-frequency features, and obtain a myocardial injury risk evaluation score with higher accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A flowchart of a myocardial injury risk evaluation method according to one embodiment;

[0042] Figure 2 A flowchart of a myocardial injury risk evaluation method according to another embodiment;

[0043] Figure 3 A block diagram of a myocardial injury risk evaluation device according to one embodiment;

[0044] Figure 4 An internal structure diagram of a computer device according to one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0046] The myocardial injury evaluation method provided by the present application can be applied to a terminal, a server, an interactive system including a terminal and a server, and is implemented through the interaction of the terminal and the server, which is not specifically limited herein. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, electrocardiogram monitoring devices and portable wearable devices, and the server can be implemented by an independent server or a server cluster composed of multiple servers.

[0047] In one embodiment, as shown in Figure 1 a myocardial injury evaluation method is provided, which is taken as an example of application to a server, and the method comprises the following steps:

[0048] S102, acquiring a resting electrocardiogram signal.

[0049] The resting electrocardiogram signal is an electrocardiogram signal collected in a resting electrocardiogram detection process. The user is in a resting state in the resting electrocardiogram detection process.

[0050] In one embodiment, the resting electrocardiogram signal of the user is collected by an electrocardiogram signal collection device in the resting electrocardiogram detection process. The electrocardiogram signal collection device can be independent of the terminal or the server and perform data transmission through Bluetooth, network, electrical connection, etc., or can be built-in as a component in the terminal or the server, so that the server and the terminal can obtain the resting electrocardiogram signal from the electrocardiogram signal collection device through data interaction / transmission, and the specific manner is not specifically limited.

[0051] S104, analyzing the resting electrocardiogram signal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardiogram signal.

[0052] The high-frequency QRS envelope curve is a shape diagram obtained by averaging the high-frequency QRS wave groups (high-frequency components of the QRS wave groups) in the resting electrocardiogram signal, which is specifically presented as a single high-frequency QRS wave group envelope curve. The high-frequency QRS envelope curve is presented through a high-frequency QRS envelope graph, in which the horizontal coordinate is time, corresponding to the duration of the QRS wave group, and the unit is ms (millisecond), and the vertical coordinate is voltage, and the unit is uV (microvolt).

[0053] Specifically, the resting ECG signal includes high frequency components and low frequency components, and the resting low frequency ECG signal can be obtained by analyzing the low frequency components in the resting ECG signal. The resting ECG signal includes a plurality of QRS complexes reflecting changes in left and right ventricular depolarization potential and time, and the high frequency QRS envelope curve can be obtained by analyzing the high frequency components of the QRS complexes in the resting ECG signal. It can be understood that the corresponding high frequency QRS envelope curve can be obtained by analyzing the high frequency components of the QRS complexes in the resting ECG signal according to the manner disclosed in the prior art. Similarly, the resting low frequency ECG signal can be obtained by analyzing the resting ECG signal in the manner disclosed in the prior art, for example, the resting low frequency ECG signal can be obtained by low-pass filtering the resting ECG signal, which will not be described here.

[0054] In S106, the high frequency QRS envelope curve is analyzed to obtain the resting high frequency QRS feature; the resting high frequency QRS feature includes the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, and the high frequency morphology index of each lead; the number of first target leads refers to the number of leads with a peak number greater than or equal to a first number threshold; the number of second target leads refers to the number of leads with a peak number equal to a second number threshold; and the number of third target leads refers to the number of leads with a peak number equal to a third number threshold.

[0055] The number of resting positive leads refers to the number of leads with a positive indicator, which can be used to characterize the myocardial cell viability. The fewer the number of resting positive leads, the stronger the myocardial cell viability. The number of resting critical leads refers to the number of leads with a critical indicator, which can be used to assist in evaluating the myocardial cell viability. The peak number refers to the total number of peaks on the high frequency QRS envelope curve, and the peak number is associated with the myocardial cell viability. The more the peak number, the more the myocardial cell viability is insufficient. The first number threshold, the second number threshold and the third number threshold can be flexibly set according to actual needs, such as 4, 3 and 2 respectively.

[0056] Specifically, the high-frequency QRS envelope curve corresponding to each lead is analyzed to obtain a total area of each amplitude reduction region on the high-frequency QRS envelope curve as a first total area, and a total area below the high-frequency QRS envelope curve as a second total area, and a ratio of the first total area to the second total area is taken as a high-frequency morphology index corresponding to the lead. The positive indicators of the respective leads are determined according to the high-frequency morphology index of each lead, and the number of leads indicated as positive by the lead positive indicators is screened and counted to obtain the number of resting positive leads, and the number of leads indicated as critical by the lead positive indicators is screened and counted to obtain the number of resting critical leads. The high-frequency QRS envelope curve of each lead is analyzed to obtain the corresponding peak number, and the peak number of each lead is statistically analyzed based on the respective quantity threshold to obtain the first target lead number, the second target lead number, and the third target lead number. It can be understood that the peak number on each high-frequency QRS envelope curve can be determined in the manner disclosed in the prior art, such as determining the maximum value number on the high-frequency QRS envelope curve as the peak number, which is not limited here.

[0057] In one embodiment, if the high-frequency morphology index of a lead is greater than or equal to a first index threshold, the lead positive indicator corresponding to the lead is identified as positive. If the high-frequency morphology index of a lead is greater than or equal to a second index threshold and less than the first index threshold, the lead positive indicator corresponding to the lead is identified as critical. The first index threshold and the second index threshold can be flexibly set according to requirements, for example, the first index threshold is 15%, and the second index threshold is 6% or 8%. The first index threshold and the second index threshold can also be dynamically adjusted according to the age of the user, which is not limited here.

[0058] In one embodiment, if the age of the user is greater than or equal to a preset age, the first index threshold is 8% and the second index threshold is 6%. If the age of the user is less than the preset age, the first index threshold is 15% and the second index threshold is 8%. The preset age can be flexibly set, such as 50 years old, which is not limited here.

[0059] In S108, the resting low-frequency electrocardio signal is analyzed to obtain a resting low-frequency feature. The resting low-frequency feature includes old myocardial infarction information.

[0060] The old myocardial infarction information is used to indicate whether there is old myocardial infarction. Specifically, referring to the way of evaluating old myocardial infarction based on ECG (electrocardiogram) in the prior art, the old myocardial infarction information is obtained by analyzing the resting low-frequency electrocardio signal, which is not repeated here. For example, it is determined that there is old myocardial infarction if there is a pathological Q wave in the resting low-frequency electrocardio signal. Also for example, it is determined that there is old myocardial infarction if there is a pathological Q wave in the resting low-frequency electrocardio signal, and the ST segment and T wave are normal.

[0061] In an embodiment, the resting low frequency feature further comprises myocardial injury information. The myocardial injury information is information for characterizing whether there is myocardial injury, which is obtained based on evaluation of the resting low frequency electrocardio signal. For example, the myocardial injury information can be obtained by analyzing the ST segment and T wave in the resting low frequency electrocardio signal. If the ST segment is elevated in a convex manner and accompanied by T wave inversion, it is determined that there is myocardial injury.

[0062] S110, evaluating the myocardial injury risk evaluation feature set to obtain a myocardial injury risk evaluation score; the myocardial injury risk evaluation feature set comprises the resting high frequency QRS feature and the resting low frequency feature.

[0063] The myocardial injury risk evaluation score is used to characterize the size of the risk of myocardial injury, and the two are positively correlated. The higher the myocardial injury risk evaluation score, the greater the risk of myocardial injury. Specifically, the myocardial injury risk evaluation feature set is input into a preconfigured myocardial injury risk evaluation function or a pre-trained myocardial injury risk evaluation model to obtain a corresponding myocardial injury risk evaluation score.

[0064] In an embodiment, the expression corresponding to the myocardial injury risk evaluation function is dynamically determined based on the myocardial injury risk evaluation feature set as input features. The myocardial injury risk evaluation function comprises a feature function corresponding to each feature in the myocardial injury risk evaluation feature set. Similarly, the input features of the myocardial injury risk evaluation model are dynamically determined based on the myocardial injury risk evaluation feature set. A corresponding myocardial injury risk evaluation model can be pre-trained for different myocardial injury risk evaluation feature sets. The input features during model training are the same as the input features during evaluation application to ensure the accuracy of the evaluation.

[0065] In an embodiment, S110 comprises: inputting each feature in the myocardial injury risk evaluation feature set into a corresponding feature function to obtain a corresponding myocardial injury risk evaluation sub-score, and summing each myocardial injury risk evaluation sub-score to obtain a myocardial injury risk evaluation score. The pre-configuration step of the feature function corresponding to each feature in the myocardial injury risk evaluation feature set comprises: segmenting the value range corresponding to the feature to obtain one or more corresponding numerical ranges; determining the feature sub-function corresponding to each numerical range according to the correlation between the feature and the myocardial injury risk and the weight of the feature; and obtaining the corresponding preconfigured feature function according to each feature sub-function corresponding to the feature.

[0066] Specifically, each feature in the feature set for myocardial injury risk assessment is respectively pre-configured with a corresponding feature function. The correlation of the feature with the myocardial injury risk includes a positive correlation and / or a negative correlation. According to the correlation of the feature with the myocardial injury risk, the value range corresponding to the feature is divided into one or more numerical ranges, and according to the correlation of the feature with the myocardial injury risk and the weight corresponding to the feature, a respective feature sub-function is determined for each numerical range, and the respective feature sub-functions corresponding to the feature are combined to obtain the corresponding pre-configured feature function. After obtaining the myocardial injury risk assessment sub-score corresponding to each feature, the myocardial injury risk assessment sub-scores are summed according to the weight of the corresponding feature to obtain the myocardial injury risk assessment score. If the myocardial injury risk assessment sub-score corresponding to the feature is greater than the weight corresponding to the feature, the myocardial injury risk assessment sub-score is reset to the weight of the corresponding feature to improve the accuracy of the assessment.

[0067] Taking the number of resting positive leads and the number of resting critical leads as examples, both of them are positively correlated with the degree of insufficient or decreased myocardial cell viability, and further positively correlated with the myocardial injury risk. Therefore, the value range of the number of resting positive leads and the number of resting critical leads can be determined as the segmented numerical range, and the value range can also be segmented into multiple numerical ranges according to the correlation of the number of resting positive leads, the number of resting critical leads and the degree of insufficient myocardial cell viability. Taking the total number of leads as 12 as an example, if the number of resting positive leads is valued as [1, 2], [3, 4], [5, 7], [8, 10], [11, 12], it respectively represents the degree of insufficient myocardial cell viability as first level, second level, third level, fourth level, and fifth level, and the higher the level represents the more serious the degree of insufficient myocardial cell viability.

[0068] In one embodiment, the value range corresponding to the number of resting positive leads and the number of resting critical leads is segmented to obtain a numerical range, and the expression of the feature function corresponding to the number of resting positive leads and the number of resting critical leads is as follows:

[0069]

[0070] wherein, is the first myocardial injury risk assessment sub-score obtained based on the number of resting positive leads and the number of resting critical leads, if , then is reset to , is the maximum value of the first myocardial injury risk assessment sub-score, which is used to represent the weight of the first myocardial injury risk assessment sub-score in the myocardial injury risk assessment score, that is, to represent the weight of the number of resting positive leads and the number of resting critical leads in the myocardial injury risk assessment, and The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, For example, when the number of resting positive leads is 35, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively,

[0071] In one embodiment, the value range corresponding to the number of resting positive leads and the number of resting critical leads is segmented into five value ranges, and the expression of the characteristic function corresponding to the number of resting positive leads and the number of resting critical leads is as follows:

[0072]

[0073] The number of resting positive leads and the number of resting critical leads, respectively, If the first myocardial injury risk assessment sub-score based on the number of resting positive leads and the number of resting critical leads is greater than 0.5, The first myocardial injury risk assessment sub-score is reset to 0.5. The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively, The number of resting positive leads and the number of resting critical leads, respectively.

[0074] The corresponding score based on the number of resting positive leads is obtained according to the following characteristic function:

[0075]

[0076] The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, For example, when the number of resting positive leads is 35, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads, The number of resting positive leads,

[0077] The corresponding score based on the number of resting critical leads is obtained according to the following characteristic function: The number of resting positive leads and the number of resting critical leads, respectively.​

[0078]

[0079] wherein, is the number of resting critical leads, , , , , are coefficients, which can be flexibly set according to , , for example, with as 35, , , , , are 0.29, 0.87, 1.46, 2.04, 2.92, respectively, and are not limited in particular.

[0080] In an embodiment, the feature function preconfigured for the first target lead number, the second target lead number, and the third target lead number is as follows:

[0081]

[0082] wherein, is a second myocardial injury risk assessment sub-score obtained based on the first target lead number, the second target lead number, and the third target lead number, if , then is reset to , is the maximum value of the second myocardial injury risk assessment sub-score, used to represent the weight of the second myocardial injury risk assessment sub-score in the myocardial injury risk assessment score, that is, used to represent the weight of the first target lead number, the second target lead number, and the third target lead number in the myocardial injury risk assessment, , and are the first target lead number, the second target lead number, and the third target lead number, respectively, , and are weights, which can be flexibly set according to actual needs, and can be flexibly set according to , , for example, with as 25, , and are 2.1, 1, and 0.5, respectively, and are not limited in particular.

[0083] In an embodiment, the feature function preconfigured for the high-frequency morphology index is as follows:

[0084]

[0085] in, The third sub-score for assessing myocardial injury risk is obtained based on the high-frequency morphological index of each lead. Then Reset to , This represents the maximum value of the third sub-score for myocardial injury risk assessment, used to characterize the weight of this sub-score within the overall myocardial injury risk assessment score. In other words, it represents the weight of the high-frequency morphological index in the myocardial injury risk assessment. This refers to the total number of leads used to acquire resting ECG signals during resting ECG monitoring. For the first The score obtained from the high-frequency morphology index of the leads. It can be obtained through the following expression:

[0086] or

[0087] in, For the first High-frequency morphology index of leads, , , , , , and The first The score of the high-frequency morphology index of the strip leads within the corresponding range can be flexibly set according to requirements, and can also be based on... Flexible settings ,and , such as Taking 25 as an example, , , , , , and The values ​​are 2.09, 1.88, 1.46, 1.05, 0.63, 0.31 and 4.18 respectively, without any specific limit here.

[0088] In one embodiment, the feature function pre-configured for the resting low-frequency feature is as follows:

[0089]

[0090] in, The fourth sub-score for myocardial injury risk assessment is obtained based on low-frequency resting characteristics. the maximum value of the fourth myocardial injury risk assessment sub-score, used to represent the weight of the fourth myocardial injury risk assessment sub-score in the myocardial injury risk assessment score, i.e., used to represent the weight of the resting low frequency feature in the myocardial injury risk assessment, the old myocardial infarction information in the resting low frequency feature, used to represent whether there is old myocardial infarction.

[0091] In one embodiment, the myocardial injury risk assessment score is the sum of the sub-scores corresponding to the features in the myocardial injury risk assessment feature set, thereby the myocardial injury risk assessment function for obtaining the corresponding myocardial injury risk assessment score according to the myocardial injury risk assessment feature set is dynamically determined by the features in the myocardial injury risk assessment feature set.

[0092] If the myocardial injury risk assessment feature set includes the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, the high frequency morphology index of each lead, and the resting low frequency feature, the expression of the preconfigured myocardial injury risk assessment function is as follows:

[0093]

[0094] wherein, the myocardial injury risk assessment score obtained based on the myocardial injury risk assessment feature set, the first myocardial injury risk assessment sub-score, the second myocardial injury risk assessment sub-score, the third myocardial injury risk assessment sub-score, the myocardial injury risk assessment sub-score, , , , obtained based on the expression in the corresponding embodiment, respectively, which will not be repeated here. , , , The respective corresponding weights can be flexibly set according to actual needs, such as 35, 25, 25 and 15, respectively, which are not specifically limited here.

[0095] The myocardial injury evaluation method, by acquiring and analyzing the resting electrocardio signal, obtains the high-frequency QRS envelope curve and the resting low-frequency electrocardio signal, and based on the high-frequency QRS envelope curve analysis, obtains the resting high-frequency QRS features such as the resting positive lead number, the resting critical lead number, the first target lead number, the second target lead number, the third target lead number and the high-frequency morphology index of each lead, and based on the resting low-frequency electrocardio signal analysis, obtains the old myocardial infarction information and other resting low-frequency features, so as to combine the resting high-frequency QRS features and the resting low-frequency features to comprehensively evaluate the myocardial injury risk, and obtain the myocardial injury risk evaluation score with higher accuracy.

[0096] In one embodiment, the resting high-frequency QRS features further include a fourth target lead number and a fifth target lead number; the fourth target lead number is the number of chest leads whose lead positive indicators indicate positive; and the fifth target lead number is the number of limb leads whose lead positive indicators indicate positive.

[0097] Specifically, the leads include chest leads and limb leads. After obtaining the corresponding lead positive indicators based on the high-frequency QRS envelope curve analysis of each lead, the number of leads whose lead positive indicators indicate positive among the chest leads is screened and counted to obtain the fourth target lead number, and the number of leads whose lead positive indicators indicate positive among the limb leads is screened and counted to obtain the fifth target lead number, so as to further combine the fourth target lead number and the fifth target lead number to evaluate the myocardial injury risk.

[0098] In one embodiment, the feature function preconfigured for the fourth target lead number and the fifth target lead number is as follows:

[0099]

[0100] wherein, is a fifth myocardial injury risk evaluation sub-score obtained based on the fourth target lead number and the fifth target lead number, if , then is reset to , is the maximum value of the fifth myocardial injury risk evaluation sub-score, used to represent the weight of the fifth myocardial injury risk evaluation sub-score in the myocardial injury risk evaluation score, that is, used to represent the weight of the fourth target lead number and the fifth target lead number in the myocardial injury risk evaluation, and are the fourth target lead number and the fifth target lead number, respectively, and are weights, which can be flexibly set according to actual needs, and specifically can be flexibly set according to , for example, if is 10, with 1 and 0.7 respectively, without being limited specifically.

[0101] In one embodiment, if the myocardial injury risk assessment feature set comprises the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, the high-frequency morphology index of each lead, and the resting low-frequency feature, and the number of fourth target leads and the number of fifth target leads, the expression of the preconfigured myocardial injury risk assessment function is as follows:

[0102]

[0103] wherein, is the myocardial injury risk assessment score obtained based on the myocardial injury risk assessment feature set, , , , and The meanings and expressions of and are respectively referred to the corresponding embodiments, which will not be described here. , , , and The respective weights of each can be flexibly set according to actual needs, such as 32, 22, 22, 14 and 10 respectively, without being limited specifically.

[0104] In the above embodiments, the number of chest leads indicated as positive by the lead positivity indicator and the number of limb leads indicated as positive by the lead positivity indicator are also used to assess the myocardial injury risk, which can further improve the accuracy of the assessment.

[0105] In one embodiment, the myocardial injury risk assessment feature set further comprises a target myocardial ischemia risk assessment score; the above myocardial injury risk assessment method further comprises: obtaining a historical myocardial ischemia risk assessment score and / or a current myocardial ischemia risk assessment score; the historical myocardial ischemia risk assessment score is obtained by analyzing historical detection data in a first time period; the current myocardial ischemia risk assessment score is obtained by analyzing current detection data in a second time period; the first time period is earlier than the second time period; and the target myocardial ischemia risk assessment score is obtained according to the historical myocardial ischemia risk assessment score and / or the current myocardial ischemia risk assessment score.

[0106] The myocardial ischemia risk assessment score is used to represent the size of the risk of myocardial ischemia, and the two are positively correlated. The higher the myocardial ischemia risk assessment score, the greater the risk of myocardial ischemia. The first time period and the second time period can be flexibly set according to actual needs. For example, the first time period is a time period ending on the day before the myocardial injury risk assessment method of the application is executed, and the time length is a preset time length. The second time period is the day when the myocardial injury risk assessment method of the application is executed. For another example, the first time period is a time period ending on the third day before the myocardial injury risk assessment method of the application is executed, and the time length is a preset time length. The second time period is a time period starting on the second day before the myocardial injury risk assessment method of the application is executed, and ending on the day when the myocardial injury risk assessment method of the application is executed. The preset time length is, for example, 1 month, 2 months or 3 months, which is not limited here. The historical detection data includes at least one of the exercise electrocardiogram signal, the resting electrocardiogram signal, the exercise myocardial injury risk detection data, the exercise treadmill test data, the ECG (electrocardiogram), the clinical symptoms, etc. collected in the first time period. The current detection data includes at least one of the exercise electrocardiogram signal, the resting electrocardiogram signal, the exercise myocardial injury risk detection data, the exercise treadmill test data, the ECG (electrocardiogram), the clinical symptoms, etc. collected at present.

[0107] Specifically, the historical myocardial ischemia risk assessment score obtained by pre-evaluation is queried and obtained, or the historical detection data collected in the first time period is queried and obtained, and the historical myocardial ischemia risk assessment score is obtained by myocardial ischemia risk assessment based on the historical detection data. And / or, the current detection data collected in the second time period is obtained, and the current myocardial ischemia risk assessment score is obtained by myocardial ischemia risk assessment based on the current detection data. If only the historical myocardial ischemia risk assessment score is obtained, the historical myocardial ischemia risk assessment score is determined as the target myocardial ischemia risk assessment score for evaluating the risk of myocardial injury. If only the current myocardial ischemia risk assessment score is obtained, the current myocardial ischemia risk assessment score is determined as the target myocardial ischemia risk assessment score. If the historical myocardial ischemia risk assessment score and the current myocardial ischemia risk assessment score are obtained at the same time, the target myocardial ischemia risk assessment score is obtained by weighted summation according to the corresponding weights. The weights corresponding to the historical myocardial ischemia risk assessment score and the current myocardial ischemia risk assessment score can be flexibly set according to actual needs, for example, 40% and 60% respectively, or 30% and 70% respectively, which is not limited here.

[0108] In one embodiment, the historical myocardial ischemia risk assessment score can be a risk assessment score automatically analyzed based on historical detection data, or a risk assessment score evaluated or corrected by a doctor based on historical detection data and user basic information, etc. Thus, the historical myocardial ischemia risk assessment score can be obtained from a local or other computer device, or the historical detection data can be obtained and analyzed to obtain the historical myocardial ischemia risk assessment score. The other computer device includes, but is not limited to, a terminal and a server. It can be understood that the historical myocardial ischemia risk assessment score can be predicted based on the historical detection data by using a preset myocardial ischemia risk assessment function or a pre-trained myocardial ischemia risk assessment model, or the historical myocardial ischemia risk assessment score can be evaluated by using other manners disclosed in the prior art, and the specific manner is not limited. If the historical detection data includes exercise ECG signals collected in the first time period, the historical myocardial ischemia risk assessment score can be evaluated by referring to the manner of evaluating the current myocardial ischemia risk assessment score based on the exercise ECG signals in the second time period.

[0109] In one embodiment, if the current detection data includes exercise ECG signals collected in the second time period, the exercise ECG signals are obtained and analyzed to obtain the current myocardial ischemia risk assessment score.

[0110] In the above embodiments, the myocardial injury is associated with myocardial ischemia, and the myocardial injury risk is evaluated in combination with the myocardial ischemia risk assessment score, which can further improve the accuracy of the myocardial injury risk assessment.

[0111] In one embodiment, obtaining the current myocardial ischemia risk assessment score includes: obtaining exercise ECG signals collected in the second time period; analyzing the exercise ECG signals to obtain a high-frequency QRS envelope curve; performing feature analysis on the high-frequency QRS envelope curve to obtain exercise high-frequency QRS features; the exercise high-frequency QRS features include the number of exercise positive leads and the number of exercise critical leads; performing evaluation analysis on a myocardial ischemia risk assessment feature set to obtain the current myocardial ischemia risk assessment score; and the myocardial ischemia risk assessment feature set includes the exercise high-frequency QRS features.

[0112] The exercise ECG signal refers to an ECG signal collected in the process of load exercise ECG detection. The user is in a motion state in the process of load exercise ECG detection to increase the cardiac load of the user through exercise. The exercise ECG signal includes a plurality of QRS complexes reflecting the changes of the left and right ventricular depolarization potential and time. Based on the QRS complexes in the exercise ECG signal, a corresponding high-frequency QRS waveform curve can be analyzed. The high-frequency QRS waveform curve can also be understood as a high-frequency QRS time-intensity curve, which is used to represent the trend of the root mean square voltage (i.e., intensity) of the high-frequency component of the QRS complex of the user with respect to time during the entire load exercise ECG detection process, that is, to reflect the energy change trend during the entire load exercise ECG detection process. The high-frequency QRS waveform curve is presented through a high-frequency QRS waveform graph. In the high-frequency QRS waveform graph, the horizontal coordinate is time, corresponding to the detection time of the load exercise ECG detection process, and the unit is min (minute). The vertical coordinate is the root mean square voltage (RMS voltage), which can also be understood as intensity or amplitude, and the unit is uV (microvolt). The exercise ECG signal includes ECG signals corresponding to each lead. Based on the exercise ECG signal, a high-frequency QRS waveform curve corresponding to each lead can be obtained. The number of exercise positive leads refers to the number of leads whose corresponding lead positive indicators indicate positivity, which can be used to assess the risk of myocardial ischemia under load exercise, and the two are positively correlated. The number of exercise critical leads refers to the number of leads whose corresponding lead positive indicators indicate criticality, which can be used to assess the critical risk of myocardial ischemia under load exercise, and in combination with the reference feature, the myocardial ischemia under load exercise can be more accurately assessed.

[0113] Specifically, similar to the resting ECG signal, the exercise ECG signal can be obtained from the ECG signal collection device through data interaction / transmission, and the specific manner is not limited. The exercise ECG signal includes high-frequency components and low-frequency components. After obtaining the exercise ECG signal, the corresponding high-frequency QRS waveform curve is obtained by analyzing the high-frequency components of the QRS complexes in the exercise ECG signal. It can be understood that the existing technology can be used to obtain the high-frequency QRS waveform curve based on the exercise ECG signal, for example, the effective QRS complexes in the exercise ECG signal are collected, aligned and averaged, and then a bandpass digital filter is used to filter the ECG signal in the frequency band of 150Hz to 250Hz, and then the HFQRS complex is subjected to root mean square processing to calculate the voltage intensity trend to obtain the high-frequency QRS waveform curve, which will not be described here.

[0114] For each lead, a point with the maximum root mean square voltage on the high-frequency QRS waveform curve in the first time period is selected as a first reference point, a point with the minimum root mean square voltage and later in time than the first reference point on the high-frequency QRS waveform curve in the first time period is selected as a second reference point, a difference between the root mean square voltage of the first reference point and the root mean square voltage of the second reference point is obtained as an amplitude drop absolute value, a ratio of the amplitude drop absolute value to the root mean square voltage of the first reference point is determined as an amplitude drop relative value, and the lead positive indicator corresponding to the lead is determined according to the amplitude drop relative value and the amplitude drop absolute value. Further, the number of leads indicated as positive by the lead positive indicators is screened and counted to obtain the number of motion positive leads, and the number of leads indicated as critical by the lead positive indicators is screened and counted to obtain the number of motion critical leads. After obtaining the motion high-frequency QRS feature from the motion electrocardio signal, the myocardial ischemia risk assessment feature set is obtained based on the motion high-frequency QRS feature, and the myocardial ischemia risk assessment score is obtained according to the myocardial ischemia risk assessment feature set, so as to further evaluate the myocardial injury risk in combination with the myocardial ischemia risk assessment score.

[0115] The first time period includes a pre-exercise period, an exercise period and a post-exercise period, the pre-exercise period is located in the resting phase, the exercise period includes the entire exercise phase, the post-exercise period is located in the recovery phase, and the pre-exercise period, the exercise period and the post-exercise period are continuous time periods. The pre-exercise period and the post-exercise period can be self-defined according to actual needs, for example, 100 seconds before exercise and 20 seconds after exercise, respectively, and the specific values are not limited.

[0116] In one embodiment, if the amplitude drop absolute value is greater than a first absolute value threshold and the amplitude drop relative value is greater than a first relative value threshold, the lead positive indicator is determined as positive. If the amplitude drop absolute value is greater than the first absolute value threshold, the amplitude drop relative value is less than or equal to the first relative value threshold and greater than a second relative value threshold, the lead positive indicator is determined as critical. The first absolute value threshold, the first relative value threshold and the second relative value threshold can be self-defined according to actual detection conditions, for example, 1 uV (microvolt), 50% and 45%, respectively, and can also be dynamically adjusted according to the age and maximum heart rate of the user, and the specific values are not limited. The second relative value threshold can also be flexibly set according to the first relative value threshold, for example, the second relative value threshold is the product of the first relative value threshold and a preset percentage, and the preset percentage can be flexibly set according to needs, for example, 90%, and the specific values are not limited.

[0117] In one embodiment, taking the second relative value threshold as the product of the first relative value threshold and a preset percentage as an example, if the user's age is less than a preset age and their maximum heart rate is greater than the target heart rate, then the first relative value threshold is 60%; if the user's age is less than a preset age and their maximum heart rate is less than or equal to the target heart rate, then the first relative value threshold is 50%; if the user's age is greater than or equal to a preset age and their maximum heart rate is greater than the target heart rate, then the first relative value threshold is 50%; if the user's age is greater than or equal to a preset age and their maximum heart rate is less than or equal to the target heart rate, then the first relative value threshold is 40%. The preset age can be customized, for example, 50 years old. The target heart rate is determined based on the user's age and a third relative value threshold, such as target heart rate = (220 - age) * third relative value threshold, where "*" indicates multiplication, and the third relative value threshold is, for example, 80% or 85%. The maximum heart rate refers to the maximum heart rate of the user during the entire stress-relief ECG monitoring process. The above are only specific examples, and in actual applications, they can be flexibly applied according to user needs, without any limitations.

[0118] In one embodiment, the pre-configured feature functions for the number of positive motion leads and the number of critical motion leads are as follows:

[0119]

[0120] in, The first sub-score for assessing myocardial ischemia risk is based on the number of positive motor leads and the number of borderline motor leads. Then Reset to , This represents the maximum value of the first sub-score for myocardial ischemia risk assessment, used to characterize the weight of the first sub-score for myocardial ischemia risk assessment in the overall myocardial ischemia risk assessment score. In other words, it represents the weight of the number of positive motor leads and the number of borderline motor leads in the myocardial ischemia risk assessment. and These represent the number of positive motor leads and the number of borderline motor leads, respectively. and All are weights and can be flexibly set according to actual needs. Specific details can be determined based on... Flexible settings , such as Taking 100 as an example, and The figures are 8.34 and 4.17 respectively, with no specific limit.

[0121] In one embodiment, if the myocardial ischemia risk assessment feature set includes the number of positive motor leads and the number of critical motor leads, then the first myocardial ischemia risk assessment sub-score is used as the current myocardial ischemia risk assessment score.

[0122] In one embodiment, if the current detection data in the second time period further comprises data other than the exercise ECG signal, the current myocardial ischemia risk assessment score can be obtained by the preset evaluation function or the pre-trained evaluation model according to the current detection data.

[0123] In the above embodiments, the exercise high-frequency QRS feature is obtained and analyzed from the exercise ECG signal, so as to further evaluate the myocardial injury risk in combination with the exercise high-frequency QRS feature, so as to improve the evaluation accuracy.

[0124] In one embodiment, the myocardial ischemia risk assessment feature set further comprises an exercise low-frequency feature; the exercise low-frequency feature is obtained by: extracting an exercise low-frequency ECG signal from the exercise ECG signal; and performing feature analysis on the exercise low-frequency ECG signal to obtain the exercise low-frequency feature; the exercise low-frequency feature comprises a ST-T segment change degree and a ST-T segment change duration of each lead.

[0125] The ST segment change comprises ST segment elevation and ST segment depression. The ST segment change degree refers to the numerical value of the ST segment change, that is, the numerical value of the ST segment elevation or depression, and is used to represent the degree of the ST segment elevation or depression. The ST segment change duration refers to the duration of the ST segment elevation or depression, and the ST segment of each heartbeat corresponding to the duration is in the state of elevation or depression.

[0126] Specifically, the exercise ECG signal comprises a high-frequency component and a low-frequency component, and the exercise low-frequency ECG signal is extracted by analyzing the exercise ECG signal. It can be understood that the exercise low-frequency ECG signal can be obtained based on the exercise ECG signal by using the manner disclosed in the prior art, for example, the exercise low-frequency ECG signal is obtained by low-pass filtering the exercise ECG signal, which will not be described here. Further, the exercise low-frequency ECG signal is analyzed to locate and extract the ST-T segment change degree and the ST-T segment change duration from the exercise low-frequency ECG signal of each lead. It can be understood that the ST segment change degree and the ST segment change duration can be obtained based on the low-frequency ECG signal of each lead by using the manner disclosed in the prior art.

[0127] In one embodiment, for each lead's low-frequency motion ECG signal, a window function is used to divide the signal into multiple signal subsets according to the time sequence and a preset step size. Each signal subset includes an ECG (electrocardiogram) corresponding to at least one heartbeat. The ECGs corresponding to each heartbeat within each signal subset are sequentially aligned and averaged to obtain the corresponding ECG (single ECG / ECG). ST-segment elevation or depression analysis is performed on the ECGs corresponding to each signal subset to obtain the degree of ST-segment change (ST-segment elevation or ST-segment depression). The maximum value among the ST-segment change degrees of each signal subset is selected as the ST-segment change degree for the corresponding lead. If ST-segment elevation is represented by a positive number and ST-segment depression by a negative number, the ST-segment change degree with the largest absolute value among the ST-segment change degrees of each signal subset is selected as the ST-segment change degree for the corresponding lead.

[0128] The first subset size is obtained by statistically analyzing the number of consecutive signal subsets in the corresponding ECG where the ST segment is elevated. The second subset size is obtained by statistically analyzing the number of consecutive signal subsets in the corresponding ECG where the ST segment is depressed. The maximum value between the first and second subset sizes is selected as the target subset size. The ST segment change duration is determined based on the duration of the window function, the target subset size, and the duration of the preset step size. Specifically, the ST segment change duration = the duration of the window function + the duration of the preset step size * (target subset size - 1). It can be understood that the durations corresponding to the window function and the preset step size can be flexibly set according to actual needs. The duration of the window function is greater than or equal to the duration of the preset step size, such as both being 1 minute, without specific limitations here.

[0129] In one embodiment, the feature function pre-configured for low-frequency motion features is as follows:

[0130]

[0131] in, The second myocardial ischemia risk assessment sub-score is obtained based on low-frequency motion characteristics. Then Reset to , This represents the maximum value of the second sub-score for myocardial ischemia risk assessment, used to characterize the weight of the second sub-score in the overall myocardial ischemia risk assessment score, that is, to characterize the weight of low-frequency motion features in myocardial ischemia risk assessment. This refers to the total number of leads used to acquire exercise ECG signals during the exercise ECG monitoring process. For the first The score obtained by the degree of ST segment change in each lead. For the first The score obtained by changing the ST segment duration in a lead. and Specifically, these can be obtained through the following expressions:

[0132]

[0133] in, For the first The degree of ST segment change in each lead, The unit for each of the corresponding numerical ranges is mV (millivolts). , and The first The score for the ST segment change in a lead within a specific range can be flexibly set according to requirements, and can also be adjusted based on... Flexible settings , such as Taking 10 as an example, , and The values ​​are 0.42, 0.25, and 0.11 respectively, without specific limitations.

[0134]

[0135] in, For the first ST segment duration changes in leads The range of its corresponding numerical values ​​is min (minutes). and The first The fraction of ST segment change duration within a certain range for a single lead can be flexibly set according to requirements, and can also be based on... Flexible settings , such as Taking 10 as an example, and The values ​​are 0.42 and 0.21 respectively, without specific limitations.

[0136] In one embodiment, if the myocardial ischemia risk assessment feature set includes the number of positive motion leads, the number of borderline motion leads, and low-frequency motion features, then the current myocardial ischemia risk assessment score is obtained based on the following expression:

[0137]

[0138] in, The current myocardial ischemia risk assessment score is obtained based on the myocardial ischemia risk assessment feature set. and The meaning and expression of are given in the corresponding embodiments and will not be repeated here. and The corresponding weights can be flexibly set according to actual needs, such as 90 and 10 respectively, without specific limitations here.

[0139] In the above embodiments, combining high-frequency QRS features with low-frequency motion features to assess myocardial ischemia risk can improve the accuracy of myocardial ischemia risk assessment scores.

[0140] In one embodiment, if the target myocardial ischemia risk assessment score is obtained by weighted summation of historical myocardial ischemia risk assessment scores and current myocardial ischemia risk assessment scores according to their respective weights, then the target myocardial ischemia risk assessment score is obtained based on the following expression:

[0141]

[0142] in, The target is the myocardial ischemia risk assessment score. and These are the current myocardial ischemia risk assessment score and the historical myocardial ischemia risk assessment score, respectively. The expressions for each are given in the corresponding embodiments and will not be repeated here. and The weights for historical and current myocardial ischemia risk assessment scores are respectively set, and can be flexibly determined according to actual needs, such as 40% and 60% respectively; no specific limit is imposed here. and The sum is 1. It can be understood that if only historical myocardial ischemia risk assessment scores are obtained, the weight of the current myocardial ischemia risk assessment score is set to 0. Similarly, if only the current myocardial ischemia risk assessment score is obtained, the weight of historical myocardial ischemia risk assessment scores is set to 0.

[0143] In one embodiment, if the myocardial injury risk assessment feature set includes the number of resting positive leads, the number of resting borderline leads, the number of first target leads, the number of second target leads, the number of third target leads, the high-frequency morphological index of each lead, the resting low-frequency characteristics, the number of fourth target leads, the number of fifth target leads, and the myocardial ischemia risk assessment score, then the expression of the pre-configured myocardial injury risk assessment function is as follows:

[0144]

[0145] in, The myocardial injury risk assessment score is obtained based on the myocardial injury risk assessment feature set. , , and The meaning and expression of are given in the corresponding embodiments, and will not be repeated here. The target myocardial ischemia risk assessment score will be used to determine the risk score for myocardial ischemia. As the sixth sub-score for myocardial injury risk assessment For specific expressions, please refer to the corresponding embodiments. The weight of the target myocardial ischemia risk assessment score in the myocardial injury risk assessment score. , , and Their respective weights, and Both can be flexibly set according to actual needs. Taking the upper limit (i.e., the maximum score) of both the myocardial injury risk assessment score and the target myocardial ischemia risk assessment score as an example, , , and Their respective weights, and They are 24%, 16%, 16%, 11%, 8%, and 25%, respectively. .

[0146] In one embodiment, the myocardial injury risk assessment feature set further includes user basic information; the above-mentioned myocardial injury risk assessment method further includes: obtaining user basic information; the user basic information includes at least one of chronic underlying disease information, alcohol consumption information, and sleep deprivation information.

[0147] User basic information includes, but is not limited to, at least one of the following: chronic underlying disease information, alcohol consumption information, sleep deprivation information, and vascular plaque information. Chronic underlying disease information indicates the presence of any chronic underlying disease. Chronic underlying diseases include, but are not limited to, hypertension, hyperlipidemia, and diabetes. Alcohol consumption information indicates the presence of a drinking habit. Sleep deprivation information indicates the presence of a sleep deprivation habit.

[0148] In one embodiment, the feature function pre-configured for user basic information is as follows:

[0149]

[0150] in, The seventh sub-score for myocardial injury risk assessment is obtained based on user basic information. This represents the maximum value of the seventh sub-score for myocardial injury risk assessment, used to characterize the weight of this sub-score within the overall myocardial injury risk assessment score. In other words, it represents the weight of user basic information in the myocardial injury risk assessment. This is basic user information used to characterize whether a user has chronic underlying diseases, drinking habits, or a habit of staying up late.

[0151] In one embodiment, if the myocardial injury risk assessment feature set comprises the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, the high frequency morphology index of each lead, the resting low frequency feature, the number of fourth target leads, the number of fifth target leads, the myocardial ischemia risk assessment score, and the user basic information, the expression of the preconfigured myocardial injury risk assessment function is as follows:

[0152]

[0153] wherein, is the myocardial injury risk assessment score based on the myocardial injury risk assessment feature set, , , , , , and have the same meaning and expression as the corresponding embodiments, which will not be repeated here. , , , , , and each correspond to a weight, and can be flexibly set according to actual needs, for example, the upper limit value (i.e., the full score) of the myocardial injury risk assessment score and the myocardial ischemia risk assessment score is 100, , , , , , and each correspond to a weight, and are 22%, 14%, 14%, 10%, 7%, 23%, and 10%, respectively, that is, .

[0154] In the above embodiments, the user basic information is also used to assess the myocardial injury risk, which can improve the accuracy of myocardial injury risk assessment.

[0155] In one or more embodiments of the present application, the coefficients or fractions of respective features in each feature function when the respective features are in each numerical range, and the weights of the respective features in the myocardial injury risk assessment, can be automatically learned and determined by the terminal and / or server according to the labeled sample set, can be manually set or corrected in the function pre-configuration stage, or can be determined by a combination of automatic learning and manual setting or correction. In one embodiment, based on the highest sub-score obtained by the feature function corresponding to the feature being greater than or equal to the weight corresponding to the feature, and less than or equal to the product of the weight corresponding to the feature and the weight coefficient, the weight coefficient can be flexibly set according to actual needs, such as 10%, which is not specifically limited here.

[0156] In one embodiment, when the myocardial injury risk is assessed by referring to the myocardial injury risk assessment score, the corresponding reference standard (such as the score threshold, the score interval, etc.) needs to be dynamically adjusted according to the feature function corresponding to each feature.

[0157] In one embodiment, in one or more embodiments of the present application, the sum of the weights of each feature in the myocardial injury risk assessment feature set is taken as 100, that is, the upper limit value (full score) of the myocardial injury risk assessment score is taken as 100. It can be understood that the sum of the weights of each feature in the myocardial injury risk assessment feature set can not be limited to 100, that is, the upper limit value (full score) of the myocardial injury risk assessment score can not be limited to 100. If the upper limit value (full score) of the myocardial injury risk assessment score is not limited to 100, when the myocardial injury risk is assessed by referring to the myocardial injury risk assessment score, the corresponding reference standard (such as the score threshold, the score interval, etc.) needs to be dynamically adjusted according to the upper limit value. Similarly, the sum of the weights of each feature in the myocardial ischemia risk assessment feature set can not be limited to 100, that is, the upper limit value (full score) of the myocardial ischemia risk assessment score can not be limited to 100.

[0158] In one embodiment, if the myocardial injury risk assessment feature set is input as an input feature into a pre-trained myocardial injury risk assessment model for prediction to obtain a myocardial injury risk assessment result, the features in the myocardial injury risk assessment feature set as input features during prediction are the same as the features in the myocardial injury risk assessment feature set as input features during model training. Thus, a corresponding myocardial injury risk assessment model can be trained for each combination of features in the myocardial injury risk assessment feature set, so as to select a corresponding myocardial injury risk assessment model for myocardial injury risk assessment according to actual conditions during prediction.

[0159] It is worth noting that the expressions and their parameters in one or more embodiments of the present application are only examples and are not used to specifically limit the meaning, values, and value ranges of the parameters.

[0160] In one embodiment, as shown in Figure 2 a myocardial injury risk assessment method is provided, which specifically comprises the following steps:

[0161] S202, acquiring a resting electrocardio signal.

[0162] S204, analyzing the resting electrocardio signal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardio signal.

[0163] S206, performing feature analysis on the high-frequency QRS envelope curve to obtain a resting high-frequency QRS feature; the resting high-frequency QRS feature includes a number of resting positive leads, a number of resting critical leads, a number of first target leads, a number of second target leads, a number of third target leads, a high-frequency morphology index of each lead, a number of fourth target leads, and a number of fifth target leads; the number of first target leads refers to the number of leads with a peak number greater than or equal to a first number threshold; the number of second target leads refers to the number of leads with a peak number equal to a second number threshold; the number of third target leads refers to the number of leads with a peak number equal to a third number threshold; the number of fourth target leads refers to the number of chest leads with a lead positive indicator indicating positive; and the number of fifth target leads refers to the number of limb leads with a lead positive indicator indicating positive.

[0164] S208, performing feature analysis on the resting low-frequency electrocardio signal to obtain a resting low-frequency feature; the resting low-frequency feature includes old myocardial infarction information.

[0165] S210, acquiring an exercise electrocardio signal collected in a second time period.

[0166] S212, analyzing the exercise electrocardio signal to obtain a high-frequency QRS envelope curve.

[0167] S214, performing feature analysis on the high-frequency QRS waveform curve to obtain an exercise high-frequency QRS feature; the exercise high-frequency QRS feature includes a number of exercise positive leads and a number of exercise critical leads.

[0168] S216, extracting an exercise low-frequency electrocardio signal from the exercise electrocardio signal.

[0169] S218, performing feature analysis on the exercise low-frequency electrocardio signal to obtain an exercise low-frequency feature; the exercise low-frequency feature includes a change degree of an ST-T segment of each lead and a change duration of the ST-T segment.

[0170] S220, performing evaluation analysis on a myocardial ischemia risk assessment feature set to obtain a current myocardial ischemia risk assessment score; the myocardial ischemia risk assessment feature set includes the exercise high-frequency QRS feature and the exercise low-frequency feature.

[0171] S222, obtain a historical myocardial ischemia risk assessment score; the historical myocardial ischemia risk assessment score is obtained by analyzing historical detection data in a first time period.

[0172] S224, obtain a target myocardial ischemia risk assessment score according to the historical myocardial ischemia risk assessment score and the current myocardial ischemia risk assessment score.

[0173] S226, obtain user basic information; the user basic information includes at least one of chronic basic disease information, drinking information, and staying up late information.

[0174] S228, obtain a myocardial injury risk assessment score by evaluating and analyzing a myocardial injury risk assessment feature set; the myocardial injury risk assessment feature set includes a resting high-frequency QRS feature, a resting low-frequency feature, a current myocardial ischemia risk assessment score, and user basic information.

[0175] In the above embodiment, the resting high-frequency QRS feature and the resting low-frequency feature are obtained and analyzed from the resting electrocardio signal, the myocardial ischemia risk assessment score is obtained and analyzed from the exercise electrocardio signal, and the user basic information associated with myocardial injury is obtained; the resting high-frequency QRS feature and the resting low-frequency feature in the resting state, the myocardial ischemia risk assessment score for representing the size of the myocardial ischemia risk, and the user basic information for evaluating the myocardial injury risk are comprehensively considered, which can improve the myocardial injury risk assessment score.

[0176] It should be understood that, although Figure 1 and Figure 2 the flowcharts show the steps in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the steps are not strictly limited in sequence, and the steps can be executed in other orders. Moreover, Figure 1 and Figure 2 at least part of the steps can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or steps or stages in other steps.

[0177] In one embodiment, as shown in Figure 3 , a myocardial injury risk assessment device 300 is provided, comprising an acquisition module 301, an analysis module 302, and an evaluation module 303, wherein:

[0178] The acquisition module 301 is configured to acquire a resting electrocardio signal.

[0179] The analysis module 302 is configured to analyze the resting electrocardiosignal to obtain a high-frequency QRS envelope curve and a resting low-frequency electrocardiosignal.

[0180] The analysis module 302 is further configured to perform feature analysis on the high-frequency QRS envelope curve to obtain a resting high-frequency QRS feature. The resting high-frequency QRS feature includes a number of resting positive leads, a number of resting critical leads, a first target lead number, a second target lead number, a third target lead number, and a high-frequency morphology index of each lead. The first target lead number refers to a number of leads with a peak number greater than or equal to a first number threshold. The second target lead number refers to a number of leads with a peak number equal to a second number threshold. The third target lead number refers to a number of leads with a peak number equal to a third number threshold.

[0181] The analysis module 302 is further configured to perform feature analysis on the resting low-frequency electrocardiosignal to obtain a resting low-frequency feature. The resting low-frequency feature includes old myocardial infarction information.

[0182] The evaluation module 303 is configured to perform evaluation analysis on a myocardial injury risk evaluation feature set to obtain a myocardial injury risk evaluation score. The myocardial injury risk evaluation feature set includes the resting high-frequency QRS feature and the resting low-frequency feature.

[0183] In one embodiment, the resting high-frequency QRS feature further includes a fourth target lead number and a fifth target lead number. The fourth target lead number refers to a number of chest leads with a lead positive indicator indicating positive. The fifth target lead number refers to a number of limb leads with a lead positive indicator indicating positive.

[0184] In one embodiment, the myocardial injury risk evaluation feature set further includes a target myocardial ischemia risk evaluation score. The acquisition module 301 is further configured to acquire a historical myocardial ischemia risk evaluation score and / or a current myocardial ischemia risk evaluation score. The historical myocardial ischemia risk evaluation score is obtained by analyzing historical detection data in a first time period. The current myocardial ischemia risk evaluation score is obtained by analyzing current detection data in a second time period. The first time period is earlier than the second time period. The target myocardial ischemia risk evaluation score is obtained according to the historical myocardial ischemia risk evaluation score and / or the current myocardial ischemia risk evaluation score.

[0185] In an embodiment, the acquisition module 301 is further configured to acquire the exercise ECG signal collected in the second time period; the analysis module 302 is further configured to analyze the exercise ECG signal to obtain a high-frequency QRS envelope curve; the exercise high-frequency QRS feature is obtained by performing feature analysis on the high-frequency QRS envelope curve; the exercise high-frequency QRS feature includes the number of exercise positive leads and the number of exercise critical leads; and the assessment module 303 is further configured to perform evaluation analysis on the set of myocardial ischemia risk assessment features to obtain the current myocardial ischemia risk assessment score; the set of myocardial ischemia risk assessment features includes the exercise high-frequency QRS feature.

[0186] In an embodiment, the set of myocardial ischemia risk assessment features further includes an exercise low-frequency feature; the analysis module 302 is further configured to extract an exercise low-frequency ECG signal from the exercise ECG signal; the exercise low-frequency feature is obtained by performing feature analysis on the exercise low-frequency ECG signal; and the exercise low-frequency feature includes the degree of ST-T segment change and the duration of ST-T segment change of each lead.

[0187] In an embodiment, the set of myocardial injury risk assessment features further includes user basic information; the acquisition module 301 is further configured to acquire the user basic information; and the user basic information includes at least one of chronic basic disease information, alcohol consumption information, and sleep deprivation information.

[0188] In an embodiment, the assessment module 303 is further configured to input each feature in the set of myocardial injury risk assessment features into a corresponding feature function to obtain a corresponding myocardial injury risk assessment sub-score, sum the myocardial injury risk assessment sub-scores to obtain the myocardial injury risk assessment score; segment the value range corresponding to the feature to obtain one or more numerical ranges corresponding to the feature; determine the feature sub-function corresponding to each numerical range according to the correlation between the feature and the myocardial injury risk and the weight of the feature; and obtain the corresponding preconfigured feature function according to the feature sub-function corresponding to the feature.

[0189] The specific limitations of the myocardial injury risk assessment device can be referred to the limitations of the myocardial injury risk assessment method described above, which will not be repeated here. Each module in the myocardial injury risk assessment device described above can be realized by software, hardware, and combinations thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0190] In an embodiment, a computer device is provided, which can be a server, and the internal structure diagram thereof can be as shown in Figure 4As shown in the figure. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store the resting electrocardio signal. The network interface of the computer device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to implement a myocardial injury risk assessment method.

[0191] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or less components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0192] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0193] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0194] In one embodiment, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0195] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiments of each method can be included. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0196] Each technical feature of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of each technical feature in the above embodiments are not described, but as long as the combination of these technical features does not exist, it should be considered as the scope of the present application.

[0197] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for assessing the risk of myocardial injury, characterized in that, The methods for assessing the risk of myocardial injury include: Obtain resting electrocardiogram signals; The resting electrocardiogram signal was analyzed to obtain the high-frequency QRS envelope curve and the resting low-frequency electrocardiogram signal; Feature analysis is performed on the high-frequency QRS envelope curve to obtain resting high-frequency QRS features. These features include the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, and the high-frequency morphology index of each lead. The number of resting positive leads refers to the number of leads whose corresponding resting lead positive indicators are positive. The number of resting critical leads refers to the number of leads whose corresponding resting lead positive indicators are critical. The high-frequency QRS envelope curve corresponding to each lead is analyzed, and the total area of ​​each amplitude reduction 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 used as the high-frequency morphology index corresponding to the lead. If the high-frequency morphology index of a lead is greater than or equal to the first index threshold, the positive index of the resting lead corresponding to that lead is marked as positive. If the high-frequency morphology index of a lead is greater than or equal to the second index threshold and less than the first index threshold, the positive index of the resting lead corresponding to that lead is marked as critical. Feature analysis is performed on the high-frequency QRS envelope curve of each lead to obtain the corresponding number of peaks. The first target lead number refers to the number of leads with a peak number greater than or equal to the first number threshold. The second target lead number refers to the number of leads with a peak number equal to the second number threshold. The third target lead number refers to the number of leads with a peak number equal to the third number threshold. The resting low-frequency electrocardiogram signal is subjected to feature analysis to obtain resting low-frequency features; the resting low-frequency features include information on old myocardial infarction; the information on old myocardial infarction is used to characterize whether old myocardial infarction exists; the presence of pathological Q waves and whether the ST segment and T wave are normal are analyzed in the resting low-frequency electrocardiogram signal. If pathological Q waves are present and the ST segment and T wave are normal, then it is determined that old myocardial infarction exists. The myocardial injury risk assessment feature set is evaluated and analyzed to obtain a myocardial injury risk assessment score; the myocardial injury risk assessment feature set includes the resting high-frequency QRS feature and the resting low-frequency feature. The myocardial injury risk assessment feature set also includes a target myocardial ischemia risk assessment score; the myocardial injury risk assessment method also includes: The historical myocardial ischemia risk assessment score and the current myocardial ischemia risk assessment score are obtained; the historical myocardial ischemia risk assessment score is obtained by analyzing historical detection data within a first time period; the current myocardial ischemia risk score is obtained by analyzing current detection data within a second time period; the first time period is earlier than the second time period; The target myocardial ischemia risk assessment score is obtained based on the historical myocardial ischemia risk assessment score and the current myocardial ischemia risk assessment score. This includes obtaining the current myocardial ischemia risk assessment score, including: Acquire the exercise electrocardiogram signals collected during the second time period; The high-frequency QRS waveform curve was obtained by analyzing the exercise electrocardiogram signal; The high-frequency QRS waveform curve is subjected to feature analysis to obtain motion high-frequency QRS characteristics. For each lead, the point with the largest root-mean-square voltage in the high-frequency QRS waveform curve within the first time period is selected as the first reference point, and the point with the smallest root-mean-square voltage after the first reference point is selected as the second reference point. The difference between the root-mean-square voltage of the first reference point and the root-mean-square voltage of the second reference point is used to obtain the absolute value of amplitude decrease. The ratio of the absolute value of amplitude decrease to the root-mean-square voltage of the first reference point is determined as the relative value of amplitude decrease. If the absolute value of amplitude decrease is greater than the first absolute value threshold and the relative value of amplitude decrease is greater than the first relative value threshold, the positive index of the motion lead is determined to be positive. If the absolute value of amplitude decrease is greater than the first absolute value threshold and the relative value of amplitude decrease is less than or equal to the first relative value threshold and greater than the second relative value threshold, the positive index of the motion lead is determined to be critical. The motion high-frequency QRS characteristics include the number of positive motion leads and the number of critical motion leads. The current myocardial ischemia risk assessment score is obtained by evaluating and analyzing the myocardial ischemia risk assessment feature set, which includes the exercise high-frequency QRS feature. The myocardial ischemia risk assessment feature set also includes low-frequency exercise features; the steps for obtaining the low-frequency exercise features include: Extract low-frequency electrocardiogram signals from the exercise electrocardiogram signals; The low-frequency ECG signal during exercise is analyzed to obtain low-frequency features during exercise; the low-frequency features during exercise include the degree of ST-T segment change and the duration of ST-T segment change in each lead.

2. The method for assessing myocardial injury risk according to claim 1, characterized in that, The resting high-frequency QRS feature also includes the number of fourth target leads and the number of fifth target leads; the number of fourth target leads is the number of chest leads that indicate positive results from the lead positive index; the number of fifth target leads is the number of limb leads that indicate positive results from the lead positive index.

3. The method for assessing myocardial injury risk according to claim 1, characterized in that, The myocardial injury risk assessment feature set also includes basic user information; the myocardial injury risk assessment method also includes: Obtain basic user information; the basic user information includes at least one of the following: chronic underlying disease information, alcohol consumption information, and sleep deprivation information.

4. The method for assessing myocardial injury risk according to any one of claims 1 to 3, characterized in that, The assessment and analysis of the myocardial injury risk assessment feature set to obtain the myocardial injury risk assessment score includes: Each feature in the myocardial injury risk assessment feature set is input into the corresponding feature function to obtain the corresponding myocardial injury risk assessment sub-score, and the myocardial injury risk assessment score is obtained by summing the sub-scores of myocardial injury risk assessment. The pre-configuration steps for the feature function corresponding to each feature in the myocardial injury risk assessment feature set include: The value range corresponding to a feature is segmented to obtain multiple corresponding numerical ranges; Based on the correlation between features and myocardial injury risk, and the weights of the features, the corresponding feature sub-functions for each numerical range are determined. The corresponding pre-configured feature functions are obtained based on the feature sub-functions corresponding to the features.

5. A myocardial injury risk assessment device for implementing the myocardial injury risk assessment method according to any one of claims 1 to 4, characterized in that, The myocardial injury risk assessment device includes: The acquisition module is used to acquire resting electrocardiogram signals; The analysis module is used to analyze the resting electrocardiogram signal to obtain the high-frequency QRS envelope curve and the resting low-frequency electrocardiogram signal; The analysis module is further used to perform feature analysis on the high-frequency QRS envelope curve to obtain resting high-frequency QRS features; the resting high-frequency QRS features include the number of resting positive leads, the number of resting critical leads, the number of first target leads, the number of second target leads, the number of third target leads, and the high-frequency morphology index of each lead; the number of first target leads refers to the number of leads with a peak count greater than or equal to a first threshold; the number of second target leads refers to the number of leads with a peak count equal to the second threshold; the number of third target leads refers to the number of leads with a peak count equal to the third threshold. The analysis module is further used to perform feature analysis on the resting low-frequency electrocardiogram signal to obtain resting low-frequency features; the resting low-frequency features include information on old myocardial infarction; The assessment module is used to evaluate and analyze the myocardial injury risk assessment feature set to obtain a myocardial injury risk assessment score; the myocardial injury risk assessment feature set includes the resting high-frequency QRS feature and the resting low-frequency feature.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the myocardial injury risk assessment method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the myocardial injury risk assessment method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the myocardial injury risk assessment method according to any one of claims 1 to 4.

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