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AuthorBoubchir L.
AuthorAl-Maadeed, Somaya
AuthorBouridane A.
Available date2022-05-19T10:23:14Z
Publication Date2014
Publication NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICASSP.2014.6854733
URIhttp://hdl.handle.net/10576/31152
AbstractThis paper proposes new time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. These features are obtained by translating and combining the most relevant time-domain and frequency-domain features into a joint time-frequency domain in order to improve the performance of EEG seizure detection and classification of non-stationary EEG signals. The optimal relevant translated features are selected according maximum relevance and minimum redundancy criteria. The experiment results obtained on real EEG data, show that the use of the translated and the selected relevant time-frequency features improves significantly the EEG classification results compared against the use of both original time-domain and frequency-domain features.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectFeature extraction
Frequency domain analysis
Neurodegenerative diseases
Neurophysiology
Signal detection
Biomedical signal processing
EEG classification
Epileptic seizure detection
Time frequency features
Time-frequency representations
Classification (of information)
TitleOn the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals
TypeConference Paper
Pagination5889-5893


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