From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

Cardiac arrhythmias are abnormal heart rhythms arising from disordered electrical dynamics that contribute significantly to global morbidity and mortality. Early prediction from physiological time series remains challenging due to nonlinear, nonstationary, and patient-specific cardiac dynamics. Although machine learning has advanced arrhythmia detection, most methods rely on static classification of electrocardiographic signals and lack online prediction, personalization, and mechanistic interpretability. From a dynamical systems perspective, arrhythmias represent regime transitions preceded by subtle deviations that are difficult to detect online. Here, we introduce CASCADE (Chaotic Attractor Sensitivity for Cardiac Anomaly Detection), an online, personalized anomaly forecasting framework based on Dynamical Systems Machine Learning (DynML). DynML uses ensembles of continuous-time nonlinear dynamical systems as chaotic reservoirs to reconstruct and predict short-term cardiac dynamics, training only a linear readout for efficient online adaptation without retraining. CASCADE identifies arrhythmia as failures of short-term predictability, quantified by statistically significant deviations between predicted and observed dynamics relative to patient-specific baselines. Performance is governed by reservoir complexity, quantified via topological entropy. Reservoirs near critical entropy regimes amplify subtle irregularities, with evidence of improved separability of early arrhythmic signatures at the readout level, particularly for morphologically distinct arrhythmia types. Evaluated on the MIT-BIH Arrhythmia Database and validated externally on the Icentia11k dataset, CASCADE achieves consistently high detection performance across diverse cardiac profiles and recording conditions. By integrating chaotic reservoir computing, entropy-guided design, and online personalization, CASCADE reframes arrhythmia detection as a dynamical regime transition problem, providing a scalable and interpretable framework for online beat-level cardiac monitoring.