Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes

Kutlu Y., KUNTALP M., GÜRKAN KUNTALP D.

EXPERT SYSTEMS WITH APPLICATIONS, vol.36, no.4, pp.7567-7575, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 4
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2008.09.052
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.7567-7575
  • Keywords: Multilayer networks, Early stopping, Noisy data, EEG, Spike detection, Epilepsy, ARTIFICIAL NEURAL-NETWORK, EEG, SYSTEM, RAW, DISCHARGES
  • Dokuz Eylül University Affiliated: Yes

Abstract

This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, "training with noise" method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures. (C) 2008 Elsevier Ltd. All rights reserved.