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Content Provider | IEEE Xplore Digital Library |
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Author | Das, Anindya Bijoy Bhuiyan, Mohammed Imamul Hassan |
Copyright Year | 2014 |
Description | Author affiliation: Department of Electrical and Electronic Engineering(Bangladesh University of Engineering and Technology, Bangladesh), Bangladesh (Das, Anindya Bijoy; Bhuiyan, Mohammed Imamul Hassan) |
Abstract | In this paper, a statistical analysis of EEG signals is carried out in the dual tree complex wavelet transform (DT-CWT) domain. It is shown that Bessel k-form(BKF) pdf can suitably model the DT-CWT sub-bands and the BKF parameters in various DT-CWT sub-bands can discriminate various types of EEG data effectively. Next these parameters are utilized by the SVM-based classifiers to classify the EEG data. The classification performance is studied for three clinically relevant cases including healthy vs seizure, non-seizure vs seizure and inter-ictal vs ictal recordings. The proposed method provides 100% accuracy with 100% sensitivity and 100% specificity in all the cases. In addition, in comparison to several state-of-the-art algorithms, the proposed method has also been shown to be computationally fast. |
Starting Page | 695 |
Ending Page | 698 |
File Size | 1011276 |
Page Count | 4 |
File Format | |
e-ISBN | 9781479941667 |
DOI | 10.1109/ICECE.2014.7026964 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-12-20 |
Publisher Place | Bangladesh |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Electroencephalography Wavelet transforms Support vector machines Brain modeling Feature extraction Epilepsy Support Vector Machine(SVM) Electroencephalogram(EEG) Seizure Dual Tree Complex Wavelet Transform(DT-CWT) Bessel K-Form(BKF) |
Content Type | Text |
Resource Type | Article |
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