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EEG signals classification using the K-means clustering and a multilayer perceptron neural network model - 2011

Eeg Signals Classification Using The K-Means Clustering And A Multilayer Perceptron Neural Network Model

Research Area:  Machine Learning


We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates.


Author(s) Name:  Umut Orhan, Mahmut Hekim, Mahmut Ozer

Journal name:  Expert Systems with Applications

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.eswa.2011.04.149

Volume Information:  Volume 38, Issue 10, 15 September 2011, Pages 13475-13481