Machine Learning in Arrhythmia and Electrophysiology

Abstract

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

“…Applications of artificial intelligence and ML in cardiac electrophysiology are emerging 54 , 146 , 147 and are discussed in more detail in a separate review of this spotlight issue. 148 In brief, the ability of ML and ‘big data’ to identify complex associations between numerous variables of interest in a data-driven, hypothesis-free approach make them attractive for identifying occult AF determinants and establishing clinical decision support systems.…”

A deep learning algorithm to translate and classify cardiac electrophysiology

“…Artificial neural networks are increasingly used to advance personalized medicine [23][24][25][26][27]. Longshort-term-memory (LSTM) based networks, which are capable of learning order dependence in sequence prediction problems [28], have been widely used for cardiac monitoring purposes [29][30][31].…”

“…Artificial neural networks (ANNs) are increasingly used to advance personalized medicine ( Alhusseini et al, 2020 ; Rogers, 2020 ; Sevakula et al, 2020 ; Jin et al, 2009 ; Trayanova et al, 2021 ). Long-short-term-memory (LSTM)-based networks, which are capable of learning order dependence in sequence prediction problems ( Hochreiter and Schmidhuber, 1997 ), have been widely used for cardiac monitoring purposes ( Guo et al, 2021 ; Shi et al, 2021 ; Picon et al, 2019 ).…”

Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

“…In the same context, researchers conducted studies to improve efficacy of targeted persistant AF ablation (Alhusseini et al, 2019;Boyle et al, 2019). Recently, few reviews report all these studies and many others related to the application of machine learning approaches to arrhythmias and electrophysiology (Cantwell et al, 2019;Feeny et al, 2020;Trayanova et al, 2021).…”