State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)

Abstract

The electrocardiogram (ECG) is one of the most common non-invasive diagnostic tools that can provide useful information regarding the patient’s health status. Deep learning (DL) is a current area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

This paper provides a systematic review of DL methods applied to ECG data for various clinical applications.

We identified 230 relevant articles published between January 2020 and December 2021 and provided a complete account of the state-of-the-art DL strategies by reporting on the number and type of hidden layers, the ECG data sources, the data preprocessing techniques, and the data splitting strategies for each one of them.

We provided a complete account of the state-of-the-art DL strategies by reporting on the number and type of hidden layers, the ECG data sources, the data preprocessing techniques, and the data splitting strategies for each one of them. We also present open research problems and point out potential gaps regarding the design and implementation of DL models.

We expect this review will provide insights into the state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.