Deep Learning Approaches for Electrocardiogram (ECG) Analysis: Challenges and Applications

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has gained significant attention for its ability to identify patterns in big healthcare datasets. Public datasets for electrocardiograms (ECGs) have been in use since the 1980s for tasks like arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have curated large ECG databases that are orders of magnitude larger than public datasets. These larger databases have demonstrated improved performance and generalizability for these tasks, as well as opened new applications in clinical scenarios. This paper provides an overview of deep learning techniques applied to ECG analysis, reviews state-of-the-art approaches, and highlights their challenges, limitations, and future opportunities.