Linear and Nonlinear Feature Extraction for Neural Seizure Detection
AuthorsElgammal, Mohamed A.
Elkhouly, Omar A.
Mohieldin, Ahmed Nader
Salama, Khaled N.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/652979
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AbstractIn 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%.
CitationElgammal MA, Elkhouly OA, Elhosary H, Elsayed M, Mohieldin AN, et al. (2018) Linear and Nonlinear Feature Extraction for Neural Seizure Detection. 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). Available: http://dx.doi.org/10.1109/MWSCAS.2018.8624031.
SponsorsThis research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST.
Conference/Event name61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018