Automated detection method of QRS wave groups of electrocardiosignals

Abstract

The invention provides an automated detection method of QRS wave groups of electrocardiosignals according to a time-domain difference threshold theory. The method comprises the concrete steps: S1, pre-processing electrocardiosignals; S2, calculating first-order time-domain fixed-point difference threshold of electrocardiosignals; S3, calculating self-adaptive threshold value; S4, detecting R wavecrest value points by combining self-adaptive threshold value and differential signals; S5, carrying out an echo detecting test on detected R wave crest value points. The automated detection method ofQRS wave groups of electrocardiosignals can be used for instruments and equipment for automatic analysis and recognition of surface electrocardiograms. Therefore, sensitivity and accuracy of automatic detection of QRS wave groups of electrocardiosignals are improved. The algorithm operation efficiency is increased.

Description

technical field [0001] The invention belongs to the technical field of medical electronics, and in particular relates to an automatic detection method for QRS wave groups in an electrocardiogram (ECG), in particular to a detection method which can be used for automatic analysis of body surface electrocardiograms and heart rhythm identification. Background technique [0002] Arrhythmia is a clinical manifestation of abnormal cardiac electrical activity, which is reflected in the abnormality of the excitability and conduction pathways of cardiomyocytes. When the arrhythmia is severe, the pumping function of the heart can be lost, which is life-threatening. According to the latest survey published in the New England Journal of Medicine in 2005, cardiovascular disease has become the leading cause of human death. In China, cardiovascular and cerebrovascular diseases are diseases with the highest annual new rate, highest recurrence rate, and high disability and death rate. Accor...

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Problems solved by technology

[0006] To sum up, although a variety of QRS complex automatic detection algorithms for ECG signals have been publicly reported, the automatic analysis, processing and identification of ECG signals still mainly rely on medical staff for such products on the market. According to clinical experience, some instruments that can be automatically diagnosed are still far from meeting the standard requirements in terms of accuracy and operating speed required by the algorithm.

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