Machine Learning in Arrhythmia and Electrophysiology.

Area of Science:

• Cardiovascular Medicine
• Artificial Intelligence
• Cardiac Electrophysiology

Background:

• Machine learning (ML) is transforming healthcare, with significant applications in cardiovascular medicine.
• While ML for ECG interpretation is well-documented, its use in other areas of cardiac electrophysiology and arrhythmia research is less explored.
• These less-known areas include basic science, experimental and computational research on arrhythmia mechanisms, advanced cardiac mapping techniques, and translational research for arrhythmia management.

Purpose of the Study:

• To comprehensively review the diverse applications of ML in cardiac electrophysiology and arrhythmia research.
• To provide foundational knowledge of ML principles and methodologies for researchers.
• To highlight research advances and discuss future challenges and perspectives in ML-driven cardiac electrophysiology.

Main Methods:

• Review of general ML principles and methodologies.
• Comprehensive review of existing literature on ML applications in cardiac electrophysiology and arrhythmia studies.
• Discussion of challenges and future perspectives.

Main Results:

• ML applications extend beyond ECG interpretation to fundamental research, experimental and computational studies of arrhythmia mechanisms, cardiac electrical function mapping, and clinical arrhythmia management.
• The review provides a structured overview of ML principles and their application in various electrophysiology research areas.
• Identified broad potential of ML approaches across different facets of cardiac electrophysiology research.

Conclusions:

• Machine learning presents significant opportunities to advance cardiac electrophysiology and arrhythmia research.
• Further exploration and application of ML methodologies are encouraged for deeper insights into arrhythmia mechanisms and improved patient management.
• Addressing challenges and embracing future perspectives will be crucial for ML-driven innovation in the field.