Cardioelectric characteristic extracting process based on evolutive wavelet wiener deconvolution

Abstract

The cardioelectric data characteristic extracting process includes the following steps: 1.preprocessing cardioelectric data; 2. extracting QRS wave group characteristic through wavelet transformation to extract the sub-frequency band of the QRS wave group from the preprocessed cardioelectric signal and the subsequent evolutive wavelet Wiener deconvolution process to extract the position of the characteristic point of QRS wave group; and 3. extracting the characteristics of P wave and T wave through substituting the time section in the QRS complex wave with base line, the subsequent wavelet transformation to extract sub-frequency band of P wave and T wave from the cardioelectric signal with the QRS wave group eliminated, and the final evolutive wavelet Wiener deconvolution process until extracting accurate characteristic point position of P wave and T wave. The present invention lays foundation for the characteristic detection.

Description

technical field [0001] The invention relates to a feature extraction method of electrocardiographic data, which belongs to the technical field of biomedical engineering. Background technique [0002] Feature extraction of ECG signals is one of the most powerful means for heart disease detection. There are several algorithms commonly used for feature extraction of ECG signals: length transform and energy transform, hidden Markov model, artificial neural network and wavelet transform. However, the above methods have certain defects: the length transformation and energy transformation cannot accurately detect the characteristic information of the abnormal QRS complex; The method also lacks effectiveness in the detection of abnormal ECG waveforms; the wavelet transform method is superior to the above-mentioned methods in the detection of pathological ECG signals, but the detection of unsteady changes in ECG waveforms, especially in the case of low signal-to-noise ratio, is diff...

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Problems solved by technology

[0004] The gist of the present invention is to propose an efficient and accurate feature extraction method for ECG data in order to overcome the problem of remote ECG data feature extraction in the remote ECG monitoring where multiple patients need to be monitored online at the same time and to detect heart disease in a timely and accurate manner. Deficiencies in cardiac disease detection in electrical monitoring

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