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.
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Hsu, CC., Yu, J. (2010). Obstructive Sleep Apnea Diagnosis from Electroencephalography Frequency Variation by Radial Basis Function Neural Network. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_29
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DOI: https://doi.org/10.1007/978-3-642-16732-4_29
Publisher Name: Springer, Berlin, Heidelberg
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