CASCADE uses chaotic reservoir computing to predict cardiac arrhythmias on a beat-to-beat basis, achieving high accuracy across diverse patient populations in the MIT-BIH dataset. Unlike static classifiers, this system treats arrhythmias as dynamical regime transitions, identifying them through failures in short-term predictability rather than pattern recognition. The approach employs topological entropy as both a performance predictor and design parameter, with reservoirs operating near critical entropy regimes optimally detecting subtle heartbeat irregularities. This represents a fundamental shift from reactive to predictive cardiac monitoring. The personalized, online adaptation capability addresses a major limitation of current arrhythmia detection systems, which typically require retraining for individual patients. For cardiovascular health, early arrhythmia prediction could enable preventive interventions before dangerous episodes occur, potentially reducing the 300,000+ annual sudden cardiac deaths in the US. However, this is a preprint awaiting peer review, and validation in real-world clinical settings beyond retrospective dataset analysis remains necessary. The dynamical systems approach, while theoretically elegant, adds computational complexity that may challenge practical implementation in wearable devices.