A Convolutional Neural Network for Arrhythmia Classification: A Review

Abstract

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. Many deep and machine learning approaches have been proposed for automatically arrhythmia classification. Convolutional neural networks (CNNs) have been achieved promising performances in this field that proved the efficiency of deep convolutional neural networks in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving.