Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/5205
Title: Optimal design of three-band orthogonal wavelet filter bank with stopband energy for identification of epileptic seizure EEG signals
Authors: Pachori, Ram Bilas
Keywords: Artificial intelligence;Biomedical signal processing;Electroencephalography;Factorization;High pass filters;Low pass filters;Matrix algebra;Band filters;EEG signal classification;Electroencephalogram signals;Feature extraction and classification;Orthogonal filter banks;Perfect reconstruction filter banks (PRFB);Regularity;Unconstrained minimization;Filter banks
Issue Date: 2019
Publisher: Springer Verlag
Citation: Bhati, D., Pachori, R. B., & Gadre, V. M. (2019). Optimal design of three-band orthogonal wavelet filter bank with stopband energy for identification of epileptic seizure EEG signals doi:10.1007/978-981-13-0923-6_17
Abstract: We design three-band orthogonal wavelet filter bank using unconstrained minimization of stopband energies of low-pass, band-pass, and high-pass filters. The analysis polyphase matrix of the orthogonal filter bank is represented by the parameterized structures such that the regularity condition is satisfied by the designed perfect reconstruction filter bank (PRFB). Dyadic and householder factorization of the analysis polyphase matrix is employed to impose perfect reconstruction, orthogonality, and regularity order of one. Three-band orthonormal scaling and wavelet functions are generated by the cascade iterations of the regular low-pass, band-pass, and high-pass filters. The designed three-band orthogonal filter bank of length 15 is used for feature extraction and classification of seizure and seizure-free electroencephalogram (EEG) signals. The classification accuracy of 99.33% is obtained from the designed filter bank which is better than the most of the recently reported results. © Springer Nature Singapore Pte Ltd 2019.
URI: https://doi.org/10.1007/978-981-13-0923-6_17
https://dspace.iiti.ac.in/handle/123456789/5205
ISBN: 9789811309229
ISSN: 2194-5357
Type of Material: Conference Paper
Appears in Collections:Department of Electrical Engineering

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