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AuthorFani, Mohammad
AuthorAzemi, Ghasem
AuthorBoashash, Boualem
Available date2014-04-27T19:18:41Z
Publication Date2011
Publication Name2011 7th International Workshop on Signal Processing and their Applications (WOSSPA)
CitationM. Fani, G. Azemi, and B. Boashash, "EEG-based automatic epilepsy diagnosis using the instantaneous frequency with sub-band energies," in Proc. of Systems, Signal Processing and their Applications (WOSSPA), 2011 7th International Workshop on, 2011, pp. 187-190
URIhttp://hdl.handle.net/10576/10995
URIhttp://dx.doi.org/10.1109/WOSSPA.2011.5931447
AbstractThis 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%.
Languageen
PublisherIEEE
Subjectelectroencephalography
feature extraction
medical signal processing
patient diagnosis
signal classification
TitleEEG-based automatic epilepsy diagnosis using the instantaneous frequency with sub-band energies
TypeConference Paper
Pagination187-190
dc.accessType Abstract Only


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