Wavelet Transform for Classification of EEG Signal using SVM and ANN

Nitendra Kumar, Khursheed Alam and Abul Hasan Siddiqi  

Department of Applied Sciences, school of Engineering and Technology, Sharda University, Greater Noida, Delhi (NCR) India,- 201306.

Corresponding Author E-mail: nkshukla.kumar4@gmail.com

Abstract: One of the most challenging research areas in the field of biomedical signal is feature extraction and classification of electroencephalogram (EEG) signal for normal and epileptic patients. Epileptic seizures are manifestations of epilepsy and epileptic seizures are disclosures of epilepsy. This paper illustrates the use of wavelet transform (WT) used for feature extraction of EEG signals and the classifiers used are Artificial Neural Network (ANN) and Support Vector Machine (SVM). For feature extraction the EEG signal is decomposed using Daubechies wavelet. Performances of classifiers are based on the parameters such as Accuracy, Sensitivity and Specificity.

Keywords: Artificial Neural Network(ANN) EEG Signal; Support Vector Machine (SVM); Wavelet Transform;

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