Machine learning integration in cardiac electrophysiology

Abstract

Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do the cardiac mapping to identify and analyze the complex heart signals patterns and to identify the location of AF responsible electrical foci. Nowadays, machine learning has become crucial in every technology field. Automation with software using machine-learning algorithms may aid electrophysiologists to do cardiac mapping without struggle and detecting electrical foci by computers. ML algorithms may identify arrhythmia compared to a board-certified cardiologist and can be developed as a very fast and reliable diagnostic tool.