ECG Arrhythmia Classification Using Convolutional Neural Network

Abstract

This study provides a thorough analysis of earlier DL techniques used to classify the ECG data. The large variability among individual patients and the high expense of labeling clinical ECG records are the main hurdles in automatically detecting arrhythmia by electrocardiogram (ECG). The classification of electrocardiogram (ECG) arrhythmias using a novel and more effective technique is presented in this research. A high-performance electrocardiogram (ECG)-based arrhythmic beats classification system is described in this research to develop a plan with an autonomous feature learning strategy and an effective optimization mechanism, based on the ECG heartbeat classification approach. We propose a method based on efficient 12-layer, the MIT-BIH Arrhythmia dataset's five micro-classes of heartbeat types and using the wavelet denoising technique. Compared to state-of-the-art approaches, the newly presented strategy enables considerable accuracy increase with quicker online retraining and less professional involvement.