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

Authors:
Yakup Kutlu;Mehmet Kuntalp;Damla Kuntalp
Affiliations:
Electrical and Electronics Engineering Department, Dokuz Eylül University, 35160 İzmir, Turkey;Electrical and Electronics Engineering Department, Dokuz Eylül University, 35160 İzmir, Turkey;Electrical and Electronics Engineering Department, Dokuz Eylül University, 35160 İzmir, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 8
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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.