Table of Contents
ISRN Biomedical Engineering
Volume 2013, Article ID 984864, 9 pages
Research Article

Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks

Electronics and Communication Department, Manipal Institute of Technology, Manipal 576104, India

Received 30 June 2013; Accepted 25 July 2013

Academic Editors: R. Grebe and V. Krajca

Copyright © 2013 Chandrakar Kamath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.