MATLAB-based ECG R-peak Detection and Signal Classification using Deep Learning Approach

This paper proposes an approach for R-peak detection employing various signal processing techniques and abnormality detection in ECG signals involving classification into heart disorder categories like different arrhythmias and premature ventricular contractions using 2D Scalograms for Continuous Wavelet Transform (CWT) of signals and deep learning. For training the neural network, we have utilized the ECG Heartbeat Categorization Dataset from Kaggle online community. We utilized the ECG samples extracted from the MIT-BIH Arrhythmia Dataset and achieved a validation accuracy of 98.32%. The strategies employed for denoising the noisy ECG signal containing random noise, White Gaussian noise, 50 Hz AC Hum and high-frequency noise achieved a signal-to-noise ratio of 12.69 after filtering.