Support vector machines for seizure detection.pdf (308.9 kB)
Download file

Support vector machines for seizure detection

Download (308.9 kB)
conference contribution
posted on 07.01.2010, 15:26 by Jonathon Chambers, Bruno Gonzalez-Velldn, Saeid Sanei
The development of a robust technique for automatic detection of the epileptic seizures is an important goal in clinical neurosciences. In this paper, the support vector machines (SVM) have been used for this purpose. The system detects and uses the three features of the electroencephalogram (EEG), namely, energy, decay (damping) of the dominant frequency, and cyclostationarity of the signals. The different types of epileptic seizures have shown some common characteristics in the feature space that can be exploited in distinguishing them from the normal activity in the brain or the nonepileptic abnormalities. The use of SVMs achieves high sensitivity and at the same time shows an improvement in terms of computational speed in comparison with the other traditional systems.



  • Mechanical, Electrical and Manufacturing Engineering


CHAMBERS, J., GONZALEZ-VELLDN, B. and SANEI, S., 2003. Support vector machines for seizure detection. IN: IEEE 3rd International Symposium on Signal Processing and Information Technology, 14-17 Dec., pp. 126-129




VoR (Version of Record)

Publication date



This is a conference paper [© IEEE]. It is also available from: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.