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Multi-lead electrocardiogram (ECG) signal composite feature extraction method and corresponding monitoring system

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 changes And other issues

Active Publication Date: 2019-04-26
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

Method used

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  • Multi-lead electrocardiogram (ECG) signal composite feature extraction method and corresponding monitoring system
  • Multi-lead electrocardiogram (ECG) signal composite feature extraction method and corresponding monitoring system
  • Multi-lead electrocardiogram (ECG) signal composite feature extraction method and corresponding monitoring system

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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 and fuse the statistical features of the morphology of all 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}, The ST-T section 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]

[0100]

[0101]

[0102] Among them, a represents the mean value of Y1, b r...

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 and fuse the statistical features and wavelet energy entropy features of the morphology of all leads;

[0123] The corresponding system differences are as follows:

[0124] The feature extraction module is used to extract the statistical features of the morphology of complex ECG waveform changes in the separated ECG digital signals and the wavelet energy entropy features of local small short-term dynamic changes;

[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 the dynamic link library to extract ...

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Abstract

The invention discloses a multi-lead electrocardiogram (ECG) signal composite feature extraction method and a corresponding monitoring system, and relates to the field of ECG signal analysis and detection. The method comprises the following steps: 1, extracting statistical features or morphological statistical features and wavelet energy entropy features of the morphology of single-lead ECG signals; 2, repeating the step 1 to acquire and fuse statistical features or the morphological statistical features and the wavelet energy entropy features of the lead morphology; the system comprises a feature extraction module; the feature extraction module comprises a dynamic link library, a feature extraction unit and a feature fusion unit, and is used for extracting the statistical features or themorphological statistical features and the wavelet energy entropy features of the morphology. The method is used for extracting the statistical features or the morphological statistical features and the wavelet energy entropy features of the morphology of the ECG signals, fully characterizes the local features of the signals, enhances the feature expression ability, and achieves the effects of accurately capturing the small short dynamic changes of the ECG signals and the morphological changes of the complex ECG waveforms as well as accurately identifying the normal state and abnormal state ofthe 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 Applications(China)
IPC IPC(8): A61B5/0402A61B5/0472A61B5/366
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
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