CtxSleepNet: Context-Aware Multimodal Sleep Staging With Label-Efficient Domain Adaptation

Generalization across recording channels, acquisition devices, and clinical centers remains a major challenge in multimodal sleep staging. We propose CtxSleepNet, a context-aware framework that integrates multimodal representation learning with adaptation tailored to the available target-domain label budget. A hierarchical CNN–GRU–Transformer encoder captures local morphology, short-term dynamics, and long-range dependencies, while an LSTM-based cross-epoch module incorporates neighboring context to refine individual epoch predictions. For label-free transfer, we introduce recording-wise domain statistics alignment, combining stage reweighting with batch normalization correction relative to source references. This strategy exploits unlabeled target data without additional pretraining or gradient-based parameter updates. For adaptation with limited labels, we develop typicality–certainty selection (TypCert), which combines target-subject selection based on stage-distribution typicality and predictive certainty with budget-dependent parameter updating. By restricting updates at low label budgets and enabling full-network fine-tuning at higher budgets, this strategy balances estimation variance and model flexibility. Experiments on four public datasets demonstrate that CtxSleepNet consistently outperforms competing methods in-domain, achieving accuracies of 79.6%–87.3%. Across six transfer scenarios spanning channel, device, and center differences, domain statistics alignment improves mean zero-shot accuracy from 68.2% to 74.4%, a gain of 6.2 percentage points. With 10% labeled target data, mean accuracy reaches 79.6%, only 2.1 percentage points below the in-domain reference of 81.7%. These results support the effectiveness of contextual multimodal modeling and complementary label-free and label-efficient adaptation for sleep staging across heterogeneous recording environments.