EEG signal classification using wavelet feature extraction and a mixture of expert model Abdulhamit Subasi * Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Avsar Yerleskesi, 46050-9 Kahramanmaras ß, Turkey Abstract Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform dis- charges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub- bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the ‘‘gating function’’. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand- alone neural network model. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete wavelet transform (DWT); Mixture of experts; Expectation-Maximization (EM) algorithm 1. Introduction Temporary electrical disturbance of the brain causes epi- leptic seizures. Sometimes seizures may go unnoticed, depending on their presentation, and sometimes may be confused with other events, such as a stroke, which can also cause falls or migraines. Approximately one in every 100 persons will experience a seizure at some time in their life (Adeli, Zhou, & Dadmehr, 2003). Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its course of action is very little understood. Research is needed for better understanding of the mechanisms caus- ing epileptic disorders. Careful analysis of the electroen- cephalograph (EEG) records can provide valuable insight into this widespread brain disorder. The detection of epi- leptiform discharges occurring in the EEG between seizures is an important component in the diagnosis of epilepsy. (Adeli et al., 2003; Subasi, 2005a). Spectral analysis of the EEG signals produces informa- tion about the brain activities. However, artificial neural net- works (ANNs) may offer a potentially superior method of EEG signal analysis to the spectral analysis methods. In con- trast to the conventional spectral analysis methods, ANNs not only model the signal, but also make a decision as to the class of signal (Subasi, 2005a; Subasi & Ercelebi, 2005). Neural networks have been successfully used in a various medical applications (Baxt, 1990; Miller, Blott, & Hames, 1992). Recent advances in the field of neural networks have made them attractive for analyzing signals. The application of neural networks has opened a new area for solving prob- lems not resolvable by other signal processing techniques (Basheer & Hajmeer, 2000; Chaudhuri & Bhattacharya, 0957-4174/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.02.005 * Tel.: +90 344 219 1253; fax: +90 344 219 1052. E-mail address: asubasi@ksu.edu.tr www.elsevier.com/locate/eswa Expert Systems with Applications 32 (2007) 1084–1093 Expert Systems with Applications