EEG signals classification using the K means clustering and a multilayer perceptron neural network model
Yazarlar (3)
Çukurova Üniversitesi, Türkiye
Prof. Dr. Mahmut HEKİM
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Bülent Ecevit Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Expert Systems with Applications |
| Dergi ISSN | 0957-4174 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 09-2011 |
| Cilt No | 38 |
| Sayı | 10 |
| Sayfalar | 13475 / 13481 |
| DOI Numarası | 10.1016/j.eswa.2011.04.149 |
| Makale Linki | http://linkinghub.elsevier.com/retrieve/pii/S0957417411006762 |
| 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. © 2010 Elsevier Ltd. All rights reserved. |
| Anahtar Kelimeler |
| Classification | Discrete wavelet transform (DWT) | EEG signals | Epilepsy | K-means clustering | Multilayer perceptron neural network (MLPNN) |
Science Direct
EEG signals classification using the K means clustering and a multilayer perceptron neural network model
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 472 |
| SCOPUS | 596 |
| Google Scholar | 831 |