Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes

Übeyli, E, Cvetkovic, D, Holland, G and Cosic, I 2009, 'Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes', Digital Signal Processing: A Review Journal, vol. Inpress, pp. 678-690.


Document type: Journal Article
Collection: Journal Articles

Title Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes
Author(s) Übeyli, E
Cvetkovic, D
Holland, G
Cosic, I
Year 2009
Journal name Digital Signal Processing: A Review Journal
Volume number Inpress
Start page 678
End page 690
Total pages 13
Publisher Academic Press
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 neuro-fuzzy 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 due to hypopnoea episodes.
Subject Biomedical Instrumentation
DOI - identifier 10.1016/j.dsp.2009.08.005
Copyright notice © 2009 Elsevier Inc. All rights reserved.
ISSN 1051-2004
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