Work place: Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, India
Research Interests: Information-Theoretic Security, Data Structures and Algorithms, Network Security, Information Security, Computational Learning Theory
Veena Desai received her PhD in cryptography and network security from the Visvesvaraya Technological University, Belagavi, India. Her research interests include cryptography, machine learning applications to security and signal analysis. She is currently is Professor in the Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India. She has published over 30 academic papers. Dr. Veena is a member of IEEE.
DOI: https://doi.org/10.5815/ijigsp.2018.01.07, Pub. Date: 8 Jan. 2018
Epilepsy is a chronic brain disorder which affects normal neuronal activity of the brain. It results in sudden repeated episodes of higher electrical activity due to sensory disturbance. Electroencephalogram (EEG) plays an important role in the diagnosis of epilepsy. Currently, manual observation of EEG is done by experienced neurologist to diagnose epilepsy and related disorders. However, automated system is a promising method for seizure detection and diagnosis. The EEG signals recorded from the patient’s scalp are preprocessed, and classified as seizure and non-seizure based on the extracted signal features. The procedure significantly eliminates the error involved in manual observation. Due to non-linear nature of EEG, joint time-frequency methods are used to analyse the EEG signals. This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT. The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT. The performance of the Least Squares Support Vector Machine (LS-SVM) classifier is tested using these features and highest classification accuracy of 99.87 % is obtained on the real time EEG database.[...] Read more.
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