IEEE transactions on bio-medical engineering · 2026
SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.
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Li Man, Bao Xiaoyu, Chen Di, Gao Wei, Qin Pengmin, Jin Xinyi, Yang Xiaochun, He Yanbin, Pan Jiahui, Li Yuanqing
Аннотация
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.
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.