Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination

Abstract

Cardiac arrhythmia can lead to morbidity and mortality and is a substantial economic burden. Electrocardiogram (ECG) monitoring is widely used to detect arrhythmia. The number of ECG recordings is increasing due to aging populations and availability of easy-to-use wearable devices. Manual interpretation of the high volume of recorded ECGs might not be a feasible and scalable solution. Therefore, machine learning algorithms are widely used for automatic ECG interpretation. In this paper, the challenges and differences between machine learning techniques for ECG monitoring and interpretation are reviewed, from traditional machine learning classifiers to deep learning and their combination.