ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 2  |  Page : 159-166

Electroencephalogram signal classification for automated epileptic seizure detection using genetic algorithm


ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India

Correspondence Address:
B Suguna Nanthini
Research Scholar, School of Computing, SASTRA University, Thanjavur - 613 401, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jnsbm.JNSBM_285_16

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Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection.


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