Automatic epilepsy detection using the instantaneous frequency and sub-band energies of the EEG signals

Mohammad Fani, Ghasem Azemi

Research output: Chapter in Book/Report/Conference proceedingConference abstractpeer-review

5 Citations (Scopus)

Abstract

In this paper, we propose a novel approach for the multiclass electroencephalogram (EEG) signals classification problem. This method uses the features derived from the instantaneous frequency and the energies of the EEG signals in different sub-bands. Results of applying the method to a publically available database reveal that, for the given classification task, the features consistently exhibit a very high degree of discrimination between the EEG signals collected from healthy and epileptic patients. Also, the analysis of the effect of the window length used during feature extraction from the EEG signals suggests that features extracted from EEG segments as short as 5 seconds achieve a very high average total accuracy of 94%.
Original languageEnglish
Title of host publication2011 19th Iranian Conference on Electrical Engineering, ICEE 2011
Place of PublicationTehran
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages1
ISBN (Electronic)9789644634284
ISBN (Print)9781457707308
Publication statusPublished - 2011
Externally publishedYes
Event19th Iranian Conference on Electrical Engineering, ICEE 2011 - Tehran, Iran, Islamic Republic of
Duration: 17 May 201119 May 2011

Conference

Conference19th Iranian Conference on Electrical Engineering, ICEE 2011
Country/TerritoryIran, Islamic Republic of
CityTehran
Period17/05/1119/05/11

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