Cardiac arrhythmia detection using deep learning: A review

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts. Traditional machine learning approaches have made significant progress in the past years. However, those methods rely on hand-crafted feature extraction, which requires in-depth domain knowledge and preprocessing of the signal (e.g., beat detection). This, plus the high variability in wave morphology among patients and the presence of noise, make it challenging for computerized interpretation to achieve high accuracy. Recent advances in deep learning make it possible to perform automatic high-level feature extraction and classification. Therefore, deep learning approaches have gained interest in arrhythmia detection. In this work, we reviewed the recent advancement of deep learning methods for automatic arrhythmia detection. We summarized existing literature from five aspects: utilized dataset, application, type of input data, model architecture, and performance evaluation. We also reported limitations of reviewed papers and potential future opportunities.