Classification of Sleep States Based on Brain Electrophysiology Assessed Using Recurrence Analysis of Single-Channel EEG


Clifton Frilot II1, David E. McCarty2,5, Paul Y. Kim3,5, Andrew A. Marino4,5

1School of Allied Health Professions, LSU Health Sciences Center, Shreveport, LA
2Colorado Sleep Institute, Boulder, CO
3Centenary College, Shreveport, LA
4Department of Neurology (Retired), LSU Health Sciences Center, Shreveport, LA
5ABR Analytics, Belcher, LA

Presented at SLEEP 2016, the 30th Annual Meeting of the Associated Professional Sleep Societies, Denver, Colorado, June 11–15, 2016


Abstract

Analysis of brain recurrence (ABR) is a method for quantifying non-randomness (law-governed complexity) in the EEG. Epochs of sleep EEG represented by four ABR variables were found to be separable into 3–12 statistically distinct clusters. The most physiologically realistic models consisted of 4–6 clusters. The choice of four clusters corresponded generally with syntactic sleep staging. The functional complexity of the brain as manifested in the EEG can be quantified objectively. The utility of ABR classification remains to be established. Clinical diagnosis of various clinical disorders including obstructive sleep apnea and excessive daytime sleepiness are areas of potential application.

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