Deep Learning-Based Approaches for ECG Signal Arrhythmia: A Comprehensive Review

An electrocardiographic signal (ECG) is a signal that is generated from the continuous rhythm of the heartbeat. An ECG signal can detect and diagnose various heart conditions, including arrhythmia. A disturbance in the electrical impulses that control the heart's contractions can result in arrhythmia, which is a term for an irregular cardiac rhythm. This disruption can cause the heart to beat too quickly, too slowly, or irregularly. Depending on the underlying cause and the severity of the condition, arrhythmias may range from innocuous to life-threatening. This article examines recent advancements in deep learning concerning ECG signal arrhythmias. This article describes methods for detecting ECG signal arrhythmia that are based on deep learning. Database on arrhythmia signal research can use MIT-BIH Arrhythmia dataset, Chine Physiological Signal Challenge, Mediplex Sejong Hospital, Computing in Cardiology Challenge, China Medical University Hospital (CMUH), PhysioNet database, UVA Holter Recordings, Medical Center in Israel, namely BIDMC and UCI Repository. Deep learning methods are the most accurate, including Deep Learning Parallel Networks, Convolutional neural networks (CNN) with long short term memory (LSTM).