Classification of ICTAL and Interictal EEG Signals

Mohammad Parvez, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)
27 Downloads (Pure)


An electroencephalogram (EEG) is a graphical record ofongoing electrical activity produced by firing of neuronsof the human brain due to internal and/or external stimuli.Feature extraction and classification of the EEG signalsare used for diagnosis the epileptic seizure (i.e., physicalchanges in behaviour that occur due to abnormal electricalactivity in the brain). Classification of Ictal (i.e., seizureperiod) and Interictal (i.e., interval between seizures) EEGsignals is very important for the treatment and precautionof an epileptic patient. However, the classificationaccuracy of Ictal and Interictal EEG signals is not atsatisfactory level due to their non-abruptness phenomenonusing the existing seizure and non-seizure classificationmethods. Moreover, the features of Ictal and Interictalsignals are not consistence in different locations for anepileptic period. In this paper we present new approachesfor features extraction of Ictal and Interictal includingvarious transformations such as discrete cosine transform(DCT), DCT-discrete wavelet transform, and singularvalue decomposition. The least square support vectormachine is applied on the features for classifications.Results demonstrate that our proposed methodsoutperform the existing state-of-the-art method in terms ofclassification accuracy for the large benchmark dataset indifferent brain locations.
Original languageEnglish
Title of host publicationIASTED
Subtitle of host publicationBIOMED 2013
EditorsAldo R Boccaccini
Place of PublicationCanada
PublisherACTA Press
Number of pages8
Publication statusPublished - 2013
EventThe 10th IASTED International Conference on Biomedical Engineering - Grand Hotel Europa, Innsbruck, Austria
Duration: 13 Feb 201315 Feb 2013


ConferenceThe 10th IASTED International Conference on Biomedical Engineering


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