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

Authors:
Umut Orhan;Mahmut Hekim;Mahmut Ozer
Affiliations:
Electronics and Computer Department, Gaziosmanpasa University, 60250 Tokat, Turkey;Electronics and Computer Department, Gaziosmanpasa University, 60250 Tokat, Turkey;Department of Electrical and Electronics Engineering, Zonguldak Karaelmas University, 67100 Zonguldak, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 16
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Abstract

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.