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A composite feature extraction method and corresponding monitoring system for multi-lead ECG signals

A technology of composite features and extraction methods, applied in applications, telemetry patient monitoring, diagnostic recording/measurement, etc., can solve the problem of low system monitoring accuracy and the inability to accurately detect local small short-term dynamic changes of ECG signals and complex ECG waveform shape changes To achieve the effect of improving comprehensiveness and accuracy, enhancing feature expression ability, and efficient and accurate monitoring

Active Publication Date: 2021-04-30
ZHENGZHOU UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is: the present invention provides a multi-lead ECG signal compound feature extraction method and corresponding monitoring system, which solves the problem that the existing feature analysis method cannot accurately detect local small short-term dynamic changes of ECG signals and complex ECG signals. Waveform shape changes, resulting in low accuracy of feature extraction and system monitoring

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  • A composite feature extraction method and corresponding monitoring system for multi-lead ECG signals
  • A composite feature extraction method and corresponding monitoring system for multi-lead ECG signals
  • A composite feature extraction method and corresponding monitoring system for multi-lead ECG signals

Examples

Experimental program
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Effect test

Embodiment 1

[0092] A method for extracting composite features of multi-lead ECG signals, comprising the steps of:

[0093] Step 1: extracting the statistical features of the morphology of the single-lead ECG signal;

[0094] Step 2: Repeat step 1 to obtain the statistical characteristics of the morphology of all leads, and then fuse the obtained statistical characteristics of the morphology of all the leads;

[0095] The statistical feature extraction of morphology includes the following steps:

[0096] Step a1: Take a certain cardiac beat in the single-lead ECG signal {X(t),t=1,...,N}, and locate the starting point, ending point and T wave ending point of its QRS wave as (x1,X (x1)), (x2, X(x2)) and (x3, X(x3)), then the QRS band can be expressed as Y1={X(t),t=x1,...,x2}, ST-T segment It can be expressed as Y2={X(t),t=x2,...,x3};

[0097] Step a2: Calculate the area C1, kurtosis coefficient C2, skewness coefficient C3 and standard deviation C4 of the QRS band:

[0098]

[0099] ...

Embodiment 2

[0120] A method for extracting composite features of multi-lead ECG signals, comprising the steps of:

[0121] Step 1: Extract the statistical features and wavelet energy entropy features of the morphology of the single-lead ECG signal;

[0122] Step 2: Repeat step 1 to obtain the statistical features and wavelet energy entropy features of all leads, and then fuse the obtained statistical features and wavelet energy entropy features of all leads;

[0123] The corresponding system differences are as follows:

[0124] Feature extraction module, for extracting the morphological statistical features or morphological statistical features and wavelet energy entropy features of each lead ECG digital signal after the separation;

[0125] The feature extraction module includes a dynamic link library, a feature extraction unit and a feature fusion unit,

[0126] A dynamic link library for encapsulating the feature extraction unit;

[0127] The feature extraction unit is used to call ...

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Abstract

The invention discloses a multi-lead electrocardiographic signal composite feature extraction method and a corresponding monitoring system, and relates to the field of electrocardiographic signal analysis and detection; the method step 1: extracting the statistical characteristics of the form of a single-lead electrocardiographic signal or the statistics of the form Feature and wavelet energy entropy feature; step 2: repeat step 1 to obtain and fuse the statistical features of all lead forms or the statistical features of form and wavelet energy entropy feature; its system includes a feature extraction module, which includes a dynamic link library and a feature extraction unit and feature fusion unit, used to extract the statistical features of the form or the statistical features of the form and the wavelet energy entropy feature; the present invention extracts the statistical features of the form of the ECG signal or the statistical feature of the form and the wavelet energy entropy feature, fully characterizing the local signal Features, enhance the ability to express features, and achieve the effect of accurately capturing the small short-term dynamic changes of ECG signals and complex ECG waveform changes, and accurately identifying the normal state and abnormal state of ECG signals.

Description

technical field [0001] The invention relates to the field of electrocardiographic signal analysis and detection, in particular to a multi-lead electrocardiographic signal composite feature extraction method and a corresponding monitoring system. Background technique [0002] The ECG signal is a comprehensive electrical signal formed by the action potentials generated by the cardiomyocytes during the regular contraction and relaxation of the heart, and is a comprehensive reflection of the electrical activity of the heart on the body surface. The conventional 12-lead or 18-lead electrocardiogram used in the hospital only samples the patient's ECG signal within 10-20 seconds, and is not sensitive to the sudden and hidden ECG changes of various arrhythmia diseases, while the dynamic electrocardiogram If the detected data cannot be read in time, the best opportunity for treatment may be missed, which is not conducive to the rescue and early warning of sudden situations. Therefor...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/318A61B5/366A61B5/358A61B5/355A61B5/00
CPCA61B5/0006A61B5/7267A61B5/318A61B5/366
Inventor 师丽韩闯王治忠牛晓可钱龙龙李泓毅
Owner ZHENGZHOU UNIV