A real-time qrs wave detection method of electrocardiographic signal

Abstract

The present invention relates to a kind of real-time QRS wave detection method of electrocardiogram signal, the steps are as follows: (1) electrocardiogram signal preprocessing, obtain the data of the electrocardiogram signal after denoising; (2) R wave detection: the part after denoising Obtaining the Amplitude Threshold A for R Wave Detection Using the Slope Method of ECG Signal t , and then use the R wave amplitude threshold A t Perform amplitude screening on all ECG signals, perform R wave detection on the ECG signals after amplitude screening, and use the average RR interval to correct the R wave detection results; (3) Q wave detection: take the R wave within a certain range The signal is differentially calculated, and the minimum value point in the result after the differential calculation is found as the Q wave position; (4) S wave detection: take the signal within a certain range after the R wave, and find the minimum value point as the S wave position. The invention can detect the QRS wave in the electrocardiogram signal quickly, effectively and in real time, and is suitable for the real-time QRS wave detection of the wearable electrocardiogram monitoring equipment.

Description

technical field [0001] The invention relates to a real-time QRS wave detection method of electrocardiographic signals. Background technique [0002] Cardiovascular disease is one of the main diseases that endanger human health today, and electrocardiogram examination is an important method for clinical diagnosis of cardiovascular disease. The parameter extraction and waveform recognition of ECG signal are the key to the analysis and diagnosis of ECG signal, and its accuracy and reliability determine the effect of diagnosis and treatment of heart disease patients. Among them, the detection of QRS wave is the basis of ECG signal analysis, and the accuracy of QRS wave group positioning directly affects the correctness of subsequent processing. [0003] In cardiovascular health monitoring, wearable ECG monitoring equipment can continuously monitor the user's heart for a long time, conveniently and effectively provide important ECG signal evidence in the dangerous period of the ...

AI Technical Summary

Problems solved by technology

Among them, the algorithm principle of the differential threshold method is simple, the calculation amount is small, the real-time performance is good, and it is easy to implement in hardware. It is easily affected by large T waves, myoelectric interference and high-frequency artifacts; the filter combination used by the mathematical morphology method is simple, the calculation speed is fast, and the real-time performance is good, but it is easy to make the QRS wave when filtering out high-frequency noise. Distortion occurs, and the accuracy rate is not high; although the wavelet transform has a high detection accuracy rate, the calculation amount is relatively large, and it requires high-performance hardware support, which is not suitable for real-time monitoring of wearable devices; the neural network method has self-learning and self-adaptive Although good discrimination results can be obtained, the algorithm takes a long time to learn and train, and the amount of calculation is too large, so it is difficult to apply to real-time detection

Images