EEG signals classification using the K means clustering and a multilayer perceptron neural network model
Yazarlar (3)
Umut Orhan Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Mahmut HEKİM Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Mahmut Ozer Zonguldak Bulent Ecevit University, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Expert Systems with Applications (Q1) | ||
| Dergi ISSN | 0957-4174 Dergi Bilgileri (2011) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2011 |
| Cilt / Sayı / Sayfa | 38 / 10 / 13475–13481 | DOI | 10.1016/j.eswa.2011.04.149 |
| Makale Linki | http://linkinghub.elsevier.com/retrieve/pii/S0957417411006762 | ||
| UAK Araştırma Alanları |
Haberleşme
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| 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. |
| 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
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| Atıf Sayıları | |
| Yöksis | 295 |
| Web of Science | 483 |
| Scopus | 609 |
| Google Scholar | 848 |