R wave rapid detection method adaptive to electrocardiogram waveform pathological change

Abstract

The invention discloses an R wave rapid detection algorithm adaptive to electrocardiogram waveform pathological change. The method, by summarizing the different characteristics of various pathological electrocardiograms such as arrhythmia, reverse wave, W wave, tall peaked P wave, tall peaked T wave and the like on an electrocardiogram signal first-order derivative and a first-order derivative square signal, can overcome the limitation of a conventional difference threshold algorithm on setting a plurality of thresholds and avoid the influence on self-adaption threshold detection due to relatively high heart rate variability among different patients through such strategies as low-threshold return-to-zero treatment, R wave classification detection as well as threshold judgment and updating for a non-classical R waveform and the like. According to the method, the algorithm is simple and easy to implement, and simultaneously the algorithm is capable of achieving rapid and accurate R wave detection on the various pathological electrocardiograms; and the algorithm is especially suitable for real-time QRS wave detection on electrocardiogram signals in mobile portable equipment. The algorithm disclosed by the invention, inspected by virtue of an MIT-BIH database, is 99.71% in sensitivity and is 99.73% in positive predictive value.

Description

technical field [0001] The invention relates to a monitoring technology, that is, a rapid detection method for R waves that can adapt to pathological changes of electrocardiogram waveforms, and is applied to related fields of electrocardiogram detection. Background technique [0002] Cardiovascular disease is the number one killer of human health, and its prevention and diagnosis are important issues that the medical community needs to face today. An electrocardiogram (ECG or EKG) is a graph generated by using an electrocardiograph to record changes in the electrical activity of the heart in each cardiac cycle from the body surface. The standard electrocardiogram mainly includes waveform features such as P wave, QRS complex wave, T wave and U wave (such as Figure 1 ), these features indicate the occurrence, propagation and recovery process of cardiac electrical excitation, and their position, shape and interval are powerful indicators for clinically detecting various heart ...

AI Technical Summary

Problems solved by technology

However, various heart diseases can cause various types of pathological waveform changes in the ECG (such as figure 2 shown), the improved differential threshold method is only suitable for relatively normal waveforms, and for various pathological changes of ECG signals such as figure 2 Arrhythmia in the heart, R wave characteristics change greatly (for example, positive wave and reverse wave appear alternately), and the processing effect is poor. At the same time, redundant threshold update calculation judgments and various retrospective rechecks reduce the operating efficiency of the algorithm. Covers up the original advantages of the differential threshold method

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