Detection of absence epileptic seizures using support vector machine

C. F. Reyes, T. J. Contreras, B. Tovar, L. I. Garay, M. A. Silva

Research output: Contribution to conferencePaper

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. © 2013 IEEE.
Original languageAmerican English
Pages132-137
Number of pages118
DOIs
StatePublished - 1 Jan 2013
Event2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013 -
Duration: 1 Jan 2013 → …

Conference

Conference2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013
Period1/01/13 → …

    Fingerprint

Cite this

Reyes, C. F., Contreras, T. J., Tovar, B., Garay, L. I., & Silva, M. A. (2013). Detection of absence epileptic seizures using support vector machine. 132-137. Paper presented at 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013, . https://doi.org/10.1109/ICEEE.2013.6676057