Publication Type : Journal Article
Publisher : IEEE
Source : IEEE Sensors Journal,( 2020), 20(9), 4914-4924. (SCIE, Impact Factor-4.325)
Url : https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8960330
Campus : Coimbatore
School : School of Computing
Year : 2020
Abstract : —In this paper, the Fourier-Bessel series
expansion based empirical wavelet transform (FBSE-EWT)
is proposed for automated alcoholism detection using
electroencephalogram (EEG) signals. The FBSE-EWT is
applied to decompose EEG signals into narrow sub-band
signals using a boundary detection approach. The
accumulated line length, log energy entropy, and norm
entropy features are extracted from different frequency
scales of narrow sub-band signals. A total of twenty features
are extracted from each attribute and out of which ten features
are from low to high frequency sub-band signals and other
ten features are from high to low frequency sub-band signals.
In order to reduce the classification model complexity,
the most significantfeatures are selected using feature selectiontechniques.Six feature ranking methods such as Relief-F,
t-test, Chi-test, relief attribute evaluation, correlation attribute evaluation, and gain ratio are used to select the most
common features based on the majority voting technique. Experiments are performed by considering top ranked 5, 10, 15,
and 20 features and classification methods such as least square support vector machine (LS-SVM), support vector
machine (SVM), and k nearest neighbor (k-NN) classifiers. The training and testing is done using leave-one out
cross-validation (LOOCV) in order to avoid over-fitting. The performances of classifiers are evaluated using accuracy,
sensitivity, and specificity measures. The results suggest that LS-SVM with radial basis function (RBF) kernel achieves
a highest average accuracy of 98.8%, sensitivity of 98.3%, and specificity of 99.1% with top 20 significant features.
Cite this Research Publication : Arti Anuragi, Dilip Singh Sisodia, and Ram Bilas Pachori . " Automated alcoholism detection using Fourier-Bessel series expansion based empirical wavelet transform. " IEEE Sensors Journal,( 2020), 20(9), 4914-4924. (SCIE, Impact Factor-4.325)