EEG-based automatic epilepsy diagnosis using the instantaneous frequency with sub-band energies

Mohammad Fani, Ghasem Azemi, Boualem Boashash

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

11 Citations (Scopus)

Abstract

This paper presents a novel approach for classifying the electroencephalogram (EEG) signals as normal or abnormal. This method uses features derived from the instantaneous frequency (IF) and energies of EEG signals in different spectral sub-bands. Results of applying the method to a database of real signals reveal that, for the given classification task, the selected features consistently exhibit a high degree of discrimination between the EEG signals collected from healthy and epileptic patients. The analysis of the effect of window length used during feature extraction indicates that features extracted from EEG segments as short as 5 seconds achieve a high average total accuracy of 95.3%.
Original languageEnglish
Title of host publication2011 7th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)
Place of PublicationAlgeria
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages187-190
Number of pages4
ISBN (Electronic)9781457706905
ISBN (Print)9781457706899
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventInternational Workshop on Systems, Signal Processing and their Applications, WOSSPA 2011 - Tipaza, Algeria
Duration: 9 May 201111 May 2011

Conference

ConferenceInternational Workshop on Systems, Signal Processing and their Applications, WOSSPA 2011
Country/TerritoryAlgeria
CityTipaza
Period9/05/1111/05/11

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