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Journal of Applied Sciences
  Year: 2014 | Volume: 14 | Issue: 14 | Page No.: 1658-1661
DOI: 10.3923/jas.2014.1658.1661
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Seizure Detection using SVM Classifier on EEG Signal

B. Suguna Nanthini and B. Santhi

Brain is the essential organ for human being because it performs the co-ordination of different specialized function of the body through neural and chemical communication. Seizures are symptoms of a brain difficulty. It happens because of sudden abnormal electrical activity in the brain. Human brain emits the electric field in waves that can be precise by the device called an electroencephalography (EEG). As done in the previous study, Gray level Cooccurrence matrix (GLCM) is used for extracting essential features from the EEG signal. Selected features are classified using the support vector machine (SVM) method. This proposed system brings forth the achievement of sensitivity, specificity and classification accuracy which are 90, 90 and 90%, respectively with simple preprocessing. According to the previous study Artificial Neural Network (ANN) was analyzed. The most important objective of this analysis is to compare the performance of classifiers such as ANN and SVM for seizure detection.
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How to cite this article:

B. Suguna Nanthini and B. Santhi, 2014. Seizure Detection using SVM Classifier on EEG Signal. Journal of Applied Sciences, 14: 1658-1661.

DOI: 10.3923/jas.2014.1658.1661






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