Abstract
Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, prob-lems with concentration, and constant stress. Finding an effective treatment for arrhythmia has become a very important endeavor for researchers and clinicians. In this article, we review the latest methodologies used in arrhythmia diagnosis and treatment. They include the application of five different types of artificial neural networks trained by machine learning and powered by artificial intelligence: convolutional, recurrent, feedforward, radial basis function, and modular neural network. Some of these methodologies are merged to enhance accuracy and efficacy. This review suggests that more research needs to be carried out in merging neural network types for their application in electrocardiogram (ECG).