Electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving

Abstract

The invention discloses an electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving and belongs to a weak bioelectrical signal processing technology field. The current electrocardiogram signal detection technology applied clinically can not give consideration to both a detection precision requirement and a real time requirement. Electrocardiogram signal pretreatment algorithm based on wavelet lifting for improving semi-soft threshold denoising and approximate envelope improving and electrocardiogram feature detection algorithm based on a slope threshold are provided in the invention. Detection criterions are set based on waveform characteristics and time domain distribution characteristics of the electrocardiogram signals. Position detections of R wave, start-stop points of the QRS waves, P wave and T wave are carried out respectively to the electrocardiogram signals. The electrocardiogram signal feature detection algorithm provided in the invention is easy, quick and suitable for parallel processing, and occupies little memory space and is convenient for DSP chip realization. Even in strong noise and P/T wave interference circumstances, R point position can be accurately detected through the algorithm provided in the invention. An R wave false detecting rate of 105 data containing serious noise disturbance is only 0.27% compared with MIT-BIH Database annotation.

Description

technical field [0001] The invention belongs to the technical field of weak bioelectrical signal processing, in particular to the denoising of weak electrocardiographic signals interfered by severe noise and the real-time detection algorithm of feature points. Background technique [0002] Since the electrocardiogram (ECG) method for diagnosing heart disease and cardiovascular diseases is non-invasive, it is convenient and easy to implement and has been widely used clinically. The ECG signals collected by non-invasive body surface electrodes are relatively weak, only at the millivolt level, so the ECG signals are easily interfered by the external environment. These interferences include power frequency interference, baseline drift, myoelectric interference, electrode contact noise, electrode polarization noise, motion interference, and internal noise of the amplifier circuit. In order to suppress all kinds of interference, enhance the effective components in the ECG signal,...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the current ECG feature detection algorithm cannot take both detection accuracy and real-time into account at the same time, and to provide a heart rate detection algorithm based on lifting wavelet improved semi-soft threshold and improved approximate envelope with high detection accuracy and strong real-time performance. Electrical signal preprocessing algorithm and detection algorithm based on slope threshold

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