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

Authors:
Elif Derya Übeyli;Dean Cvetkovic;Gerard Holland;Irena Cosic
Affiliations:
TOBB Ekonomi ve Teknoloji Üniversitesi, Faculty of Engineering, Department of Electrical and Electronics Engineering, 06530 Söğütözü, Ankara, Turkey;RMIT University, School of Electrical and Computer Engineering, GPO Box 2476V, Melbourne, VIC 3001, Australia;St. Luke's Hospital, Sleep Centre, Sydney, NSW, Australia;RMIT University, Science Engineering and Technology, GPO Box 2476V, Melbourne, VIC 3001, Australia
Venue:
Digital Signal Processing
Year:
2010

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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.