Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Öz This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy. Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy. We used three classifiers with the feature vectors: TreeBoost, Random Forests, and support vector machines. We carried out experiments using a real life EEG signals data set that is available from the University of Bonn Hospital in Germany. This paper shows the effect of combining together signal energy with different EEG frequency band waves in order to classify epilepsy, and that this combination has computed 97.5% accuracy over using feature vectors with fewer band wave transformations (84-95.5% accuracy), using the TreeBoost algorithm and 10 folds cross validation. This combination computed 99% specificity and 95.5% sensitivity. Furthermore, the paper demonstrates and analyses the effectiveness of using ensemble based tree learning.

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Kaynak Göster

Bibtex @araştırma makalesi { ijamec450951, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {2147-8228}, eissn = {2147-8228}, address = {}, publisher = {Selçuk Üniversitesi}, year = {2017}, volume = {}, pages = {36 - 41}, doi = {10.18100/ijamec.2017SpecialIssue30468}, title = {Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands}, key = {cite}, author = {Bellegdi, Sameh A. and Arafat, Samer M. A.} }
APA Bellegdi, S , Arafat, S . (2017). Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands . International Journal of Applied Mathematics Electronics and Computers , Special Issue (2017) , 36-41 . DOI: 10.18100/ijamec.2017SpecialIssue30468
MLA Bellegdi, S , Arafat, S . "Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands" . International Journal of Applied Mathematics Electronics and Computers (2017 ): 36-41 <
Chicago Bellegdi, S , Arafat, S . "Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands". International Journal of Applied Mathematics Electronics and Computers (2017 ): 36-41
RIS TY - JOUR T1 - Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands AU - Sameh A. Bellegdi , Samer M. A. Arafat Y1 - 2017 PY - 2017 N1 - doi: 10.18100/ijamec.2017SpecialIssue30468 DO - 10.18100/ijamec.2017SpecialIssue30468 T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 36 EP - 41 VL - IS - Special Issue-1 SN - 2147-8228-2147-8228 M3 - doi: 10.18100/ijamec.2017SpecialIssue30468 UR - Y2 - 2021 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands %A Sameh A. Bellegdi , Samer M. A. Arafat %T Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands %D 2017 %J International Journal of Applied Mathematics Electronics and Computers %P 2147-8228-2147-8228 %V %N Special Issue-1 %R doi: 10.18100/ijamec.2017SpecialIssue30468 %U 10.18100/ijamec.2017SpecialIssue30468
ISNAD Bellegdi, Sameh A. , Arafat, Samer M. A. . "Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands". International Journal of Applied Mathematics Electronics and Computers / Special Issue-1 (Eylül 2017): 36-41 .
AMA Bellegdi S , Arafat S . Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands. International Journal of Applied Mathematics Electronics and Computers. 2017; (Special Issue-1): 36-41.
Vancouver Bellegdi S , Arafat S . Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands. International Journal of Applied Mathematics Electronics and Computers. 2017; (Special Issue-1): 36-41.
IEEE S. Bellegdi ve S. Arafat , "Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands", International Journal of Applied Mathematics Electronics and Computers, sayı. Special Issue-1, ss. 36-41, Eyl. 2017, doi:10.18100/ijamec.2017SpecialIssue30468