C-SHARE: Cross-Subject Heterogeneity-Aware Retrieval Augmentation for Early CAP Phase A Forecasting

Cyclic alternating pattern (CAP), an established EEG marker of NREM sleep instability and fragmentation, comprises recurrent Phase A activations alternating with Phase B intervals. Forecasting Phase A onsets could enable anticipatory monitoring and inform future interventions aimed at improving sleep quality. Realizing this potential requires overcoming three challenges: sparse event annotations, physiologically heterogeneous precursors, and generalization to unseen subjects. Parametric forecasters pool these limited observations into stable population-level predictions. Retrieval augmentation can use the same training data differently, retrieving source events as empirical corrections, but the retrieved observations do not necessarily constitute independent or transferable evidence. We introduce C-SHARE, a model-agnostic plug-in retrieval-augmentation module for frozen EEG forecasters. C-SHARE groups overlapping pre-onset windows into onset-indexed episodes, learns future-conditioned multi-subspace keys for predictive matching, and gates each correction using event- and subject-level support. In addition, we contribute NapCAP-128Ch, to our knowledge the first high-density CAP EEG dataset and the largest CAP-annotated EEG cohort by subject count, comprising 120 subjects. Across eight backbones and three horizons on this cohort and the public CAP Sleep Database, C-SHARE improves over the corresponding frozen backbone in AUPRC in all 48 dataset–backbone–horizon settings and in AUROC in 42 of 48, yielding more consistent gains than general-purpose retrieval baselines.