Assessing Disorders of Consciousness Using Temporal Sleep Dynamics Extracted From Whole-Night PSG

Accurate assessment of patients with disorders of consciousness (DoC) remains a major clinical challenge due to the limitations of behavior-based evaluations and task-dependent neurophysiological paradigms. Whole-night polysomnography (PSG), a passive and noninvasive monitoring tool, offers unique potential for revealing residual brain function during sleep. In this study, we propose a temporal-dynamic feature extraction and aggregation framework for PSG analysis to enable machine learning-based diagnosis and prognosis in DoC patients. Whole-night EEG/EOG signals were segmented into non-overlapping 30-second epochs, from which time-domain, spectral, and nonlinear complexity features were extracted. To obtain a unified and compact representation of variable-length feature sequences, two aggregation strategies were applied: stage- wise averaging based on sleep staging and clustering-based grouping via unsupervised learning. A two-stage feature selection pipeline further reduced dimensionality while preserving discriminative power and interpretability. Classifiers trained on the aggregated features achieved strong performance in distinguishing minimally conscious state (MCS) from vegetative state (VS), with AUC values exceeding 0.84, and demonstrated robust predictive ability for long-term recovery outcomes (AUC=0.79). These findings highlight the diagnostic and prognostic value of whole-night PSG and support the development of fully automated, task-free assessment tools for DoC.