Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Erguzel T. T., Ozekes S., Tan O., Gultekin S.

CLINICAL EEG AND NEUROSCIENCE, cilt.46, sa.4, ss.321-326, 2015 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 4
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1177/1550059414523764
  • Dergi Adı: CLINICAL EEG AND NEUROSCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.321-326
  • Anahtar Kelimeler: genetic algorithm, artificial neural network, cordance, major depressive disorder, rTMS, TRANSCRANIAL MAGNETIC STIMULATION, TREATMENT RESPONSE, RELEVANT FEATURES, MAJOR DEPRESSION, EEG, PREDICTOR, PERFUSION
  • Marmara Üniversitesi Adresli: Hayır

Özet

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.