Key result
Novel continual learning framework improves detection accuracy for sparse pathological events like AF.
Why the study?
Sparse pathological events in physiological signals pose challenges for continual learning models to balance plasticity and stability, capture multi-scale temporal dependencies, and prevent forgetting during long-term monitoring.
A novel continual learning framework with cross-scale attention improves the detection of sparse physiological events like atrial fibrillation and seizures while mitigating catastrophic forgetting.
May support sparse-event detection in long-term monitoring; leaves open need for prospective clinical validation.
With rapid global population aging, chronic diseases have become a major public health challenge. Early detection and continuous analysis of physiological signals are essential for timely intervention, particularly in long-term monitoring scenarios. However, pathological events such as atrial fibrillation and epileptic seizures are often highly sparse over time, posing challenges for continuously adaptive models. Three key challenges arise in this setting. First, non-stationary physiological data streams require models to balance plasticity and stability. Second, insufficient modeling of both short-term dynamics and long-range temporal dependencies limits the representation of pathological patterns across multiple temporal scales. Third, continual model updates under sparse pathological supervision can lead to catastrophic forgetting of previously learned abnormal patterns. To address these challenges, we propose a continual learning (CL) framework for pathological event detection in physiological time series under evolving data distributions. The framework incorporates a dual-path memory system consisting of a working memory for rapid adaptation and a long-term memory for stable knowledge consolidation. The framework further employs a Cross-Scale Attention (CSA) mechanism to capture short-term dynamics and long-range temporal dependencies. An anomaly-prioritized replay strategy and a memory-consistency objective are further introduced to preserve rare pathological patterns during continual adaptation. We evaluate the proposed framework on three public datasets — MIT-BIH, CHB-MIT, and Siena — for atrial fibrillation detection and seizure detection. Experimental results show competitive and generally superior performance in accuracy and F1 score across the evaluated datasets, while BWT results demonstrate improved knowledge retention during continual learning. These results demonstrate the effectiveness of the proposed framework for detecting sparse pathological events under evolving physiological data distributions.
No takes yet. Share an insight, caveat, or question.
Zhao et al. (2026) studied Atrial fibrillation and epileptic seizures. Continual learning framework with dual-path memory system and Cross-Scale Attention was evaluated on Accuracy and F1 score for atrial fibrillation and seizure detection. A continual learning framework with a dual-path memory system and cross-scale attention demonstrated competitive and generally superior accuracy and F1 score for detecting sparse pathological events.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: