Runtime Verification for Clinically Interpretable Arrhythmia Classification

Alex Baird, Srinivas Pinisetty, Nathan Allen, Nitish D. Patel, Partha S. Roop

Abstract: Automatic detection of cardiac arrhythmia is an important tool in the fight against cardiovascular diseases and their associated human impacts. Such detection needs to be both accurate and timely, in order to allow for interventions to be administered within short time frames. Traditionally, such approaches have used black box implementations which are not explainable and hence have limited use in terms of clinical interpretability. Additionally, these implementations may either require additional training between patients, or have processing times which make them unsuitable for real-time classification. To address this, we develop a set of formal Timed Automaton-based policies that capture three common arrhythmia, Premature Ventricular Contraction, Ventricular Tachycardia, and Atrial Fibrilation, in terms of Electrocardiogram (ECG) features. We synthesise Runtime Verification monitors for each of these policies, and run them alongside existing clinical ECG databases to evaluate their efficacy. This approach shows comparable results to existing black box work with accuracies ranging from 90 % to 96 % while still being both explainable and clinically interpretable.