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We propose a novel unsupervised approach for sleep dynamics exploration andautomatic annotation by combining modern harmonic analysis tools. Specifically,we apply diffusion-based algorithms, diffusion map (DM) and alternatingdiffusion (AD) algorithms, to reconstruct the intrinsic geometry of sleepdynamics by reorganizing the spectral information of an electroencephalogram(EEG) extracted from a nonlinear-type time frequency analysis tool, thesynchrosqueezing transform (SST). The visualization is achieved by thenonlinear dimension reduction properties of DM and AD. Moreover, thereconstructed nonlinear geometric structure of the sleep dynamics allows us toachieve the automatic annotation purpose. The hidden Markov model is trained topredict the sleep stage. The prediction performance is validated on a publiclyavailable benchmark database, Physionet Sleep-EDF [extended] SC* and ST*, withthe leave-one-subject-out cross validation. The overall accuracy and macro F1achieve 82:57% and 76% in Sleep-EDF SC* and 77.01% and 71:53% in Sleep-EDF ST*,which is compatible with the state-of-the-art results by supervisedlearning-based algorithms. The results suggest the potential of the proposedalgorithm for clinical applications.

Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques