TY - GEN
T1 - Linear and Nonlinear Feature Extraction for Neural Seizure Detection
AU - Elgammal, Mohamed A.
AU - Elkhouly, Omar A.
AU - Elhosary, Heba
AU - Elsayed, Mohamed
AU - Mohieldin, Ahmed Nader
AU - Salama, Khaled N.
AU - Mostafa, Hassan
N1 - 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.
PY - 2019/2/26
Y1 - 2019/2/26
N2 - 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%.
AB - 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%.
UR - http://hdl.handle.net/10754/652979
UR - https://ieeexplore.ieee.org/document/8624031
UR - http://www.scopus.com/inward/record.url?scp=85062213441&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2018.8624031
DO - 10.1109/MWSCAS.2018.8624031
M3 - Conference contribution
SN - 9781538673928
SP - 795
EP - 798
BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -