An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis

Abstract EN

R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters.

In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease.

This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals.

After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal.

Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal.

The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment.

The R-wave detection rates were 99.93% and 99.98%, respectively.

Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.