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المؤلفBoubchir L.
المؤلفAl-Maadeed, Somaya
المؤلفBouridane A.
تاريخ الإتاحة2022-05-19T10:23:14Z
تاريخ النشر2014
اسم المنشورICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ICASSP.2014.6854733
معرّف المصادر الموحدhttp://hdl.handle.net/10576/31152
الملخصThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعFeature 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)
العنوانOn the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals
النوعConference Paper
الصفحات5889-5893
dc.accessType Abstract Only


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