A Survey on ECG-Based Classification of Cardiac Arrhythmias Using Convolutional Neural Networks

Abstract

Recently, concern for cardiac health has increased, leading to the development of neural network models to diagnose arrhythmias. This study presents a systematic review of current approaches in the classification of cardiac arrhythmias with convolutional neural networks. The predominant databases, the most common arrhythmia types, preprocessing techniques,
the most applied convolutional neural network models,
and the most used evaluation metrics are addressed.
The findings show a trend towards diagnoses such as
normal sinus rhythm, left bundle branch block and right
bundle branch block. In preprocessing, the use of filters
to reduce noise in electrocardiogram signals,
segmentation and balancing of the records is
highlighted. The MIT-BIH arrhythmia database was
identified as the most used in studies. Finally, the
effectiveness of convolutional neural network models
combined with long short-term memory networks and
transformer-based attention modules is highlighted.