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Communication Dans Un Congrès Année : 2020

Detection of Epileptic High Frequency Oscillations Using Support Vector Machines

Résumé

Recently, several studies have proved that High Frequency Oscillations (HFOs) of [80500] Hz are reliable biomarkers for delineating the epileptogenic zone. The total duration of HFOs is extremely short compared to the entire duration of EEG dataset to be analyzed. Therefore, visual marking of HFOs is timeconsuming and laborious process. In order to promote the clinical use of HFOs oscillations as reliable biomarkers of epileptogenic tissue and to conduct large-scale investigations on cerebral HFOs activities, several automatic detection techniques have been proposed over the past few years. In the present framework, we propose a novel approach for detecting HFOs based on Support Vector Machines (SVM). Our method is subsequently compared with six other methods. HFOs detection performance is evaluated in terms of sensitivity, false discovery rate, area under the ROC curve and execution time. Our results demonstrate that SVM approach yields low false detection (FDR = 6.36%) but, in its current implementation, is moderately sensitive to detect HFOs with a sensitivity of 71.06%. © 2020 IEEE.
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Dates et versions

hal-03102526 , version 1 (07-01-2021)

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Citer

S. Chaibi, F. Krikid, C. Mahjoub, T. Lajnef, R. Le Bouquin Jeannes, et al.. Detection of Epileptic High Frequency Oscillations Using Support Vector Machines. 5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020, Sep 2020, Sfax, Tunisia. pp.9231905, ⟨10.1109/ATSIP49331.2020.9231905⟩. ⟨hal-03102526⟩
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