Abstract
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.
Highlights
ECG (Electrocardiogram) represents the myocardial electrical activity of the heart
ECG signals play an important role in the diagnosis of cardiovascular diseases, such as arrhythmia, hypertension, or ischemic heart disease
Researchers have developed a number of algorithms, for example, based on the digital filter [3], wavelet transform [4, 5], neural network [6,7,8], image segmentation [9], and so on
Summary
An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis. 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. Is 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 reshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. E 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. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring
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