Linear and Nonlinear Feature Extraction for Neural Seizure Detection

Mohamed A. Elgammal, Omar A. Elkhouly, Heba Elhosary, Mohamed Elsayed, Ahmed Nader Mohieldin, Khaled N. Salama, Hassan Mostafa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


In this paper, both linear and nonlinear features have been reviewed with linear support vector machine (SVM) classifier for neural seizure detection. The work introduced in the paper includes performance measurement through different metrics: accuracy, sensitivity, and specificity of multiple linear and nonlinear features with linear support vector machine (SVM). A comparison is performed between the performance of different combinations between 11 linear features and 9 nonlinear features to conclude the best set of features. It is found that some features enhance the detection performance greatly. Using a combination of 3 features of them, a linear SVM classifier detects seizures with sensitivity of 96.78%, specificity of 97.9%, and accuracy of 97.9%.
Original languageEnglish (US)
Title of host publication2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781538673928
StatePublished - Feb 26 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST.


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