Episodic Context Memory: Patient-State Adaptation for Longitudinal Cardiovascular Monitoring

Physiological classifiers trained on short, independently sampled recordings are often deployed on continuous streams from unseen individuals, where observations become temporally correlated and patient-specific. We study this mismatch as longitudinal personalization and introduce Episodic Context Memory (ECM), a stateful adaptation framework that keeps a pretrained population predictor fixed while learning how to construct and retrieve patient-specific context. Using labeled streams from separate calibration patients, ECM learns shared rules for writing preceding observations into a compact multi-slot memory and retrieving context relevant to the current query. At deployment on an unseen patient, only the memory state evolves, without target-patient labels or gradient updates. We evaluated atrial fibrillation (AF) detection from the electrocardiogram (ECG) on patient-disjoint internal and external cohorts against supervised fine-tuning, test-time adaptation, prototype methods, recurrent conditioning, and neural memory. Under external dataset shift, the primary ECM variant increased AUROC by +1.1% and decreased AF burden error by -1.2% relative to the best comparator, and an AF-specific extension increased macro-F1 by +2.7% and AUROC by +2.1%. In longitudinal analyses, ECM approaches achieved the highest macro-F1 improvement of up to +15.1%. Ablations identified patient-specific context and query-conditioned retrieval as the main contributors, while evaluations on additional cardiovascular tasks and sensing modalities provided promising evidence beyond AF detection. These results show that patient-state adaptation can provide robust, label-free personalization for longitudinal deployment under distribution shift.