Machine learning models for automated interpretation of 12-lead electrocardiographic signals: a narrative review of techniques, challenges, achievements and clinical relevance

Background and Objective: Novel advances in machine learning (ML) and its subfield, deep learning (DL), as well as the recent release of large-scale electrocardiogram (ECG) databases, have driven a sharp increase in research related to automated ECG interpretation. This review aims to summarize the recent ML approaches for automatically interpreting standard 12-lead ECG signals.