ECG-Based Arrhythmia Detection by A Shallow CNN Model

Abstract Recent advances in biomedical applications have focused a lot of emphasis on the detection of which could be caused by cardiovascular disease (CVD) The electrocardiogram (ECG), which depicts the electrical activity of the heart, is the foundation for arrhythmia analysis. Different machine learning methods used on ECG datasets have demonstrated excellent performance in detecting arrhythmias. Nevertheless, feature extraction is necessary for machine learning algorithms. Modern deep learning techniques don't require feature extraction because they learn all the parameters simultaneously, in contrast to these techniques. In this study, a 1D CNN approach is presented and tested on the arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH). The proposed model, which only has three layers, attained an accuracy of 97.40%.