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Title: Support vector machines for seizure detection
Authors: Chambers, Jonathon
Gonzalez-Velldn, Bruno
Sanei, Saeid
Keywords: Electroencephalography
Medical signal detection
Medical signal processing
Neurophysiology
Patient diagnosis
Support vector machines
Issue Date: 2003
Publisher: © IEEE
Citation: 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
Abstract: 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.
Description: This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/servlet/opac?punumber=9296. 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.
Version: Published
DOI: 10.1109/ISSPIT.2003.1341076
URI: https://dspace.lboro.ac.uk/2134/5754
ISBN: 0780382927
Appears in Collections:Conference Papers and Contributions (Electronic, Electrical and Systems Engineering)

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