A Lightweight CNN to Identify Cardiac Arrhythmia Using 2D ECG Images

Abstract

Worldwide, cardiac arrhythmia disease has become one of the most frequent heart problems, leading to death in most cases. In fact, cardiologists use the electrocardiogram (ECG) to diagnose arrhythmia by analyzing the heartbeat signals and utilizing electrodes to detect variations in the heart rhythm if they show certain abnormalities. Indeed, heart attacks depend on the treatment speed received, and since its risk is increased by arrhythmias, in this chapter the authors create an automatic system that can detect cardiac arrhythmia by using deep learning algorithms. They propose a deep convolutional neural network (CNN) to automatically classify five types of arrhythmias then evaluate and test it on the MIT-BIH database. The authors obtained interesting results by creating five CNN models, testing, and comparing them to choose the best performing one, and then comparing it to some state-of-the-art models. The authors use significant performance metrics to evaluate the models, including precision, recall, sensitivity, and F1 score.