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

Orhan U., Hekim M., Ozer M.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.38, sa.10, ss.13475-13481, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 10
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.eswa.2011.04.149
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.13475-13481
  • Anahtar Kelimeler: Epilepsy, K-means clustering, Discrete wavelet transform (DWT), Multilayer perceptron neural network (MLPNN), EEC signals, Classification, WAVELET TRANSFORM, EPILEPTIC SEIZURES, AUTOMATIC RECOGNITION, LOGISTIC-REGRESSION, ALERTNESS LEVEL, COEFFICIENTS, PREDICTION
  • Çukurova Üniversitesi Adresli: Hayır

Özet

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. (C) 2011 Elsevier Ltd. All rights reserved.