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Abstract
In the past decades, wavelet transforms are widely employed for characterizing the electroencephalogram (EEG) signals for automatic diagnosis of epileptic seizure. But few vital issues like the classification of epileptic seizure types from normal EEG signals has not yet been benefited with wavelet transforms. Hence, in this paper, the two major types of wavelet transform, namely discrete wavelet transform (DWT) and wavelet packet decomposition (WPD) are employed for the automatic diagnosis of the epileptic seizure and its types. The publicly available KITS EEG database consisting of three groups namely, normal, focal epilepsy and generalized epilepsy are utilized in this work. Four experimental cases namely (i) normal-generalized epilepsy, (ii) normal-focal epilepsy, (iii) normal-focal-generalized and (iv) normal-epilepsy are used to investigate the proposed approach. Further, this paper attempts to identify the best wavelet function from the commonly used seven wavelet families and the level of decomposition required to analyse the EEG signals. The nine statistical features are extracted from the wavelet coefficients and fed into the support vector machine (SVM) classifier. From the experimental result it was found out that the DWT with rbio1.1 attained the highest classification accuracy for all the experimental cases.
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Notes on contributors
N. J. Sairamya
Sairamya N J received her BE degree in electrical and electronics engineering from the CSI College of Engineering, The Nilgiris, India in 2010, the ME degree in VLSI design from the Karpagam University, Coimbatore, India, in 2012. She is currently working as a Junior Research Fellow and pursuing the PhD degree in the Department of Electronics and Communication Engineering, at the Karunya Institute of Technology and Sciences, Coimbatore, India. Her research interests include biomedical signal processing, image processing and pattern recognition. Email: [email protected]
M. Joel Premkumar
Joel Premkumar M received his BTech degree in electrical and electronics engineering from the Karunya Institute of Technology and Sciences, Coimbatore, India. His research interests include biomedical signal processing and machine learning. Email: [email protected]
S. Thomas George
S Thomas George is a doctorate in the area of biosignal processing. He completed his BE and ME from Bharathiar University and PhD from Karunya University. He has been working at the Karunya University for the past 20 years and 6 months have acquired good teaching and research experience. To his credit, he has publications in reputed international journals and in a number of conference proceedings. His areas of interests are biosignal processing, image processing and optimization techniques. He is currently working as associate professor in electronics and communication engineering, Karunya University, Coimbatore, India.
M. S. P. Subathra
M S P Subathra is a doctorate in the area of power systems, machine learning and optimization techniques. She has completed her BE and ME degrees from Thiagarjar College of Engineering, Madurai Kamaraj University and PhD from Karunya University. She has been working at the Karunya University for the past 20 years and 7 months have acquired good teaching and research experience. To her credit, she has publications in reputed international journals and in number of conference proceedings. Her areas of interests are power systems, renewable energy and optimization techniques. She is currently working as an associate professor in electrical and electronics engineering, Karunya University, Coimbatore, India. Email: [email protected]