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arXiv · 1805.07300

Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference

Leon Chlon·Andrew Song·Sandya Subramanian·Hugo Soulat·John Tauber·Demba Ba·Michael Prerau
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Abstract

Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epochs of EEG time-domain traces into 5 discrete sleep stages. Unfortunately, this scoring process is subjective and time-consuming, and the defined stages do not capture the heterogeneous landscape of healthy and clinical neural dynamics. This motivates the search for a data-driven and principled way to identify the number and composition of salient, reoccurring brain states present during sleep. To this end, we propose a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), combined with wide-sense stationary (WSS) time series spectral estimation to construct a generative model for personalized subject sleep states. In addition, we employ multitaper spectral estimation to further reduce the large variance of the spectral estimates inherent to finite-length EEG measurements. By applying our method to both simulated and human sleep data, we arrive at three main results: 1) a Bayesian nonparametric automated algorithm that recovers general temporal dynamics of sleep, 2) identification of subject-specific "microstates" within canonical sleep stages, and 3) discovery of stage-dependent sub-oscillations with shared spectral signatures across subjects.

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Leon Chlon, Andrew Song, Sandya Subramanian, Hugo Soulat, John Tauber, Demba Ba, Michael Prerau. 2018-05-18. Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference. https://arxiv.org/abs/1805.07300

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