REM-Flow: Robust and Extensible Multi-Center Sleep Staging via Generative Knowledge Replay

Sleep staging is an indispensable step for assessing sleep quality and diagnosing disorders, relying heavily on mining actionable clinical knowledge from complex physiological time series. However, establishing robust and generalizable knowledge representations in multi-center contexts remains a fundamental challenge in medical data mining. This is primarily due to distribution shifts arising from subject variability and device heterogeneity. Existing solutions exhibit limited global robustness in multi-center expansion scenarios, meaning adaptation to new centers leads to catastrophic forgetting of pre-learned knowledge. Furthermore, conventional mitigation strategies often require storing raw historical data, which is incompatible with the medical privacy constraints of multi-center data sharing. To bridge these gaps, we propose REM-Flow, a Robust and Extensible Multi-Center sleep staging framework for multi-center continuous adaptation scenarios. At its core lies a novel generative knowledge-replay strategy that consolidates cross-center knowledge by reconstructing the latent distributions of historical domains under privacy-preserving constraints on data sharing. By synergizing with a hybrid knowledge distillation objective, the model actively adapts to new centers while effectively mitigating catastrophic forgetting. Extensive experiments on public sleep datasets demonstrate that REM-Flow achieves superior performance in both knowledge retention and continuous adaptation, offering an extensible and more privacy-preserving solution for clinical deployment.