Title :
Detection of absence epileptic seizures using support vector machine
Author :
Reyes, C.F. ; Contreras, T.J. ; Tovar, Blanca ; Garay, L.I. ; Silva, Mario A.
Author_Institution :
Maestria en Tecnol. Av., UPIITA, Mexico City, Mexico
fDate :
Sept. 30 2013-Oct. 4 2013
Abstract :
An application of support vector machine is presented as a tool for events detection in the electroencephalogram recorded from a patient clinically diagnosed with absence epilepsy. A comparison of five kernels is shown (linear, quadratic, polynomial, RBP and MLP) evaluating their efficiency for the detection of this epileptic event occurrence. The kernel with the best performance is the quadratic, with 99.43% accuracy in this specific case.
Keywords :
electroencephalography; patient diagnosis; MLP; RBP; absence epilepsy; absence epileptic seizures; electroencephalogram; epileptic event occurrence; events detection; linear kernel; patient diagnosis; polynomial kernel; quadratic kernel; support vector machine; EEG; SVM; epilepsy; kernels;
Conference_Titel :
Electrical Engineering, Computing Science and Automatic Control (CCE), 2013 10th International Conference on
Conference_Location :
Mexico City
Print_ISBN :
978-1-4799-1460-9
DOI :
10.1109/ICEEE.2013.6676057
