Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6197
Title: Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes
Authors: Übeyli, Elif Derya
Cvetkovic, Dean
Holland, Gerard
Cosic, Irena
Keywords: Adaptive neuro-fuzzy inference system (ANFIS)
Wavelet coefficients
Sleep apnoea hypopnoea
Electroencephalogram (EEG)
Issue Date: 2010
Publisher: Academic Press Inc Elsevier Science
Abstract: The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neurofuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity clue to hypopnoea episodes. (C) 2009 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2009.08.005
https://hdl.handle.net/20.500.11851/6197
ISSN: 1051-2004
1095-4333
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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