Classification of Obstructive Sleep Apnea by Neural Networks

Authors:
Zhongyu Pang;Derong Liu;Stephen R. Lloyd
Affiliations:
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607-7053, USA;Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607-7053, USA;Center for Narcolepsy, Sleep, and Health Research, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612-7350, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Electroencephalogram (EEG) is a common tool to explore brain activities ranging from concentrated cognitive efforts to sleepiness. For the issue of sleepiness, pupil behavior can provide some information regarding alertness. The issue of sleepiness can be reflected by EEG energy. Specifically, intrusion of EEG theta wave activity into the beta activity of active wakefulness has been interpreted as ensuing sleepiness. This paper develops an innovative signal classification method that is capable of differentiating subjects with sleep disorder of obstructive sleep apnea (OSA) which cause excessive daytime sleepiness (EDS) from normal control subjects who do not have a sleep disorder. The theta energy ratios are calculated from the 2-second sliding windows by Fourier transform. An artificial neural network of modified ART2 is utilized to identify subjects with OSA from a combined group of subjects including healthy controls. This grouping from the neural network is then compared with the actual diagnostic classification of subjects as OSA or healthy controls and it is found to be 91% accurate in differentiating between the two groups.