Automatic Classification of Cardiac Arrhythmias Using Deep Learning Techniques: A Systematic Review

Abstract

Cardiac arrhythmias are one of the main causes of death worldwide; therefore, early detection is essential to save the lives of patients who suffer from them and to reduce the cost of medical treatment. The growth of electronic technology, combined with the great potential of Deep Learning (DL) techniques, has enabled the design of devices for early and accurate detection of cardiac arrhythmias. This article presents a Systematic Literature Review (SLR) using a Systematic Mapping study and Bibliometric Analysis, through a set of relevant research questions (RQs), in relation to DL techniques applied to the automatic detection and classification of cardiac arrhythmias using electrocardiogram (ECG) signals, during the period 2017-2023. The PRISMA 2020 methodology was employed to identify the most pertinent scholarly articles, by querying the following databases: Scopus, IEEE Xplore, and PhysioNet Challenges, resulting in 494 publications being retrieved. This study also included a bibliometric analysis aimed at tracing the evolution of the primary technologies utilized in the automatic detection and recognition of cardiac arrhythmias. Additionally, it evaluates the performance of each technology, offering insights crucial for guiding future research.

Keywords

• Cardiac arrhythmia
• convolution neural network
• deep learning
• electrocardiogram
• systematic literature review
• classification