DocumentCode :
3521565
Title :
Support vector machines for seizure detection
Author :
González-Vellón, Bruno ; Sanei, Saeid ; Chambers, Jonuthon A.
Author_Institution :
Centre for Digital Signal Process. Res., King´´s Coll., London, UK
fYear :
2003
fDate :
14-17 Dec. 2003
Firstpage :
126
Lastpage :
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.
Keywords :
electroencephalography; medical signal detection; medical signal processing; neurophysiology; patient diagnosis; support vector machines; brain activity; clinical neurosciences; dominant frequency damping; electroencephalogram; epileptic seizures; seizure detection; signal cyclostationarity; support vector machines; Biological neural networks; Damping; Digital signal processing; Educational institutions; Electroencephalography; Epilepsy; Matching pursuit algorithms; Patient monitoring; Robustness; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Information Technology, 2003. ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on
Print_ISBN :
0-7803-8292-7
Type :
conf
DOI :
10.1109/ISSPIT.2003.1341076
Filename :
1341076
Link To Document :
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