Obstructive sleep apnea diagnosis from electroencephalography frequency variation by radial basis function neural network

Authors:
Chien-Chang Hsu;Jie Yu
Affiliations:
Department of Computer Science and Information Engineering, Fu-Jen Catholic University, Taipei, Taiwan;Department of Computer Science and Information Engineering, Fu-Jen Catholic University, Taipei, Taiwan
Venue:
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2010

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Abstract

This paper proposes an obstructive sleep apnea diagnosis system based on electroencephalography frequency variations. The system uses a band-pass filter to remove extremely low and high frequency in brainwave. The system then uses baseline correction and the Hilbert-Huang transform to extract the features from the filtered signals. Moreover, the system uses a radial basis function neural network to diagnose the kind of obstructive sleep apnea from electroencephalography. Experimental results show that the system can achieve over 96%, 92%, and 97% accuracy for obstructive sleep apnea, Obstructive sleep apnea with arousal, and arousal. The system provides a feasible way for the technicians of sleep center to interpret the EEG signal easily and completely.