Paper Info
Title
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference.
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.
Year
Venue
Field
2018
arXiv: Machine Learning
Hierarchical Dirichlet process,Spectral density estimation,Pattern recognition,Multitaper,Sleep disorder,Artificial intelligence,Hidden Markov model,Machine learning,Electroencephalography,Mathematics,Sleep Stages,Generative model
DocType
Volume
Citations 
Journal
abs/1805.07300
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Leon Chlon100.34
Andrew Song283.98
Sandya Subramanian300.68
Hugo Soulat400.68
John Tauber500.34
Demba E. Ba615017.80
Michael J. Prerau700.34