SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

OBJECTIVE

Clinical assessment of disorders of consciousness (DOC) remains challenging because motor impairments and fluctuating vigilance may obscure residual awareness. Sleep electroencephalography (EEG) provides a non-participatory window into residual brain function; however, pathological recordings are scarce and heterogeneous, fine-grained stage annotation is unreliable, andexisting methods rely on multi-channel acquisition with limited cross-subject generalization. This study aims to develop a single-channel EEG framework for sleep staging and sleep-informed consciousness assessment in DOC patients.

METHODS

SleepConFormer integrates a multi-task EEG representation learning (MTERL) backbone pretrained on public sleep datasets, a Stage Confusion Estimation Transformer (SCE-Transformer) for confusion-aware temporal modeling under pathological conditions, and a logit space aggregation strategy for robust Wake-Sleep and Wake-NREM-REM inference. Stage predictions and stage conditioned EEG embeddings are aggregated into subject level features for minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) discrimination under subject-independent evaluation.

RESULTS

On three public sleep datasets from non-DOC participants, Sleep ConFormer achieves 84.5-87.7% five-class accuracy with strong cross-dataset generalization (average MF1 78.73%). In a clinical DOC cohort (n = 24), coarse Wake-Sleep staging reaches 80.78% accuracy. Sleep-derived features distinguish MCS from UWS with 91.7% accuracy and 0.846 AUC, surpassing single-modal features by 12.5%.

CONCLUSION

SleepConFormer demonstrates the feasibility of transferable single-channel sleep staging and sleep informed consciousness discrimination in DOC under subject-independent internal validation.

SIGNIFICANCE: This work provides a promising proof-of-concept framework for sleep-informed DOC assessment using single-channel EEG and supports future investigation toward objective bedside monitoring in neurocritical care.