Unsupervised Clustering of Extensive Physiological Features Substantiates Five-Stage Sleep Staging Paradigm.

Abstract

Traditional sleep staging, guided by the AASM scoring manual, categorizes sleep into five discrete stages based on visual analysis of electrophysiological signals by human expert. However, the rationale for the staging number remains underexplored, and sleep scoring results show low inter-rater agreement, due to such possible factors as subjective judgment, expertise variability among human experts, and limited number of signal features in the AASM manual. To address these limitations, we developed an unsupervised clustering framework incorporating a large set of features from EEG, EOG and EMG signals, including but not limited to the AASM visual features, and performing sleep staging without relying on pre-defined scoring rules. This data-driven approach shows that the sleep data can be optimally partitioned into five clusters, which correspond well to the five sleep stages defined in the AASM scoring manual. Importantly, the algorithm recognizes over 80% of AASM visual features, and additionally uncovers many features not mentioned in the AASM scoring manual. Detailed analysis into epochs inconsistently scored by the algorithm and by the human expert shows that the algorithm provides more interpretable results. The present study offers well-grounded evidence supporting that sleep should be partitioned into five stages. The findings also suggest that more features in the sleep data should be utilized in addition to those included in the AASM scoring manual for more accurate sleep scoring.