Abstract

Arrhythmia management has been revolutionized by the ability to monitor the cardiac rhythm in a patient's home environment in real-time using high-fidelity prescription-grade and commercially available wearable electrodes. The vast amount of digitally acquired electrophysiological signals has generated the need for scalable and efficient data processing with actionable output that can be provided directly to clinicians and patients. In this setting, artificial intelligence applications are increasingly important in arrhythmia monitoring, ranging from conventional algorithmic analysis for rhythm determination to more complex deep machine learning methods that have led to the realization of fully automated humanlike rhythm determination in real-time.

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