Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/123456789/19087
Title: Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification
Authors: BHATI, D
SHARMA, M
PACHORI, RB
GADRE, VM
Keywords: Intrinsic Mode Functions
Compactly Supported Wavelets
Artificial Neural-Networks
Infiltration Parameters
Perfect Reconstruction
Orthonormal Wavelets
Design
Transform
Decomposition
Construction
Issue Date: 2017
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation: DIGITAL SIGNAL PROCESSING,62,259-273
Abstract: In this paper, we design time-frequency localized three-band biorthogonal linear phase wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification. Time-frequency localized analysis and synthesis low-pass filters (LPF) are designed using convex semidefinite programming (SDP) by transforming a nonconvex problem into a convex SDP using semidefinite relaxation technique. Three band parameterized lattice biorthogonal linear phase perfect reconstruction filter bank (BOLPPRFB) is chosen and nonlinear least squares algorithm is used to determine its parameters values that generate the designed analysis and synthesis LPF such that the band-pass and high-pass filters are also well localized in time and frequency domain. The designed analysis and synthesis three-band wavelet filter banks are compared with the standard two-band filter banks like Daubechies maximally regular filter banks, Cohen-Daubechies-Feauveau (CDF) biorthogonal filter banks and orthogonal time-frequency localized filter banks. Kruskal-Wallis statistical test is employed to measure the statistical significance of the subband features obtained from the various two and three-band filter banks for epileptic seizure EEG signal classification. The results show that the designed three-band analysis and synthesis filter banks both outperform two-band filter banks in the classification of seizure and seizure-free EEG signals. The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification. (C) 2016 Elsevier Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/j.dsp.2016.12.004
http://localhost:8080/xmlui/handle/123456789/19087
ISSN: 1051-2004
1095-4333
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