Casting Ventricular Arrhythmia Detection as Anomaly Detection via One-Class Meta-Learning

Ventricular arrhythmia detection is a critical yet challenging task in cardiac healthcare due to the rarity of abnormal episodes and the high inter-patient variability in cardiac signals. These challenges are further exacerbated in implantable cardioverter-defibrillators, which operate under stringent memory and computational constraints. In this paper, we analyze inter- and intra-patient variability using dimensionality reduction and divergence metrics, and leverage these observations to formulate ventricular arrhythmia detection as a deployment-aligned one-class meta-learning problem. Accordingly, we adopt a one-class formulation of model-agnostic meta-learning (OC-MAML) with a clinically grounded task design that reflects real-world deployment conditions. Specifically, patient-disjoint support and query sets are used to simulate realistic distribution shifts and inter-patient variability. By training primarily on normal intracardiac electrogram segments, the OC-MAML-based framework learns a task-agnostic initialization that rapidly adapts to new patients using only a few normal samples, thereby substantially reducing dependence on labeled arrhythmic data. Compared to conventionalvanillamodel-agnostic meta-learning (MAML), our proposed OC-MAML-based framework achieves a relative improvement of +14.1% in sensitivity, +2.5% in balanced accuracy, and +4.1% inF1-score, while reducing adaptation time by 5× and maintaining comparable memory efficiency. These results underscore the framework’s potential for scalable, nearly label-free deployment in edge-based cardiac monitoring systems. The code for the proposed framework is publicly available at https://github.com/jaradat/VAD-OC-MAML.