02427nas a2200181   4500000000100000000000100001008004100002100001800043700002000061700001700081700002500098700001900123245012200142856007200264490000700336520188800343022001402231       2022                            d1 aPawel Plawiak1 aNilima Salankar1 aSaeed Qaisar1 aRyszard Tadeusiewicz1 aMohamed Hammad00aEEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning.  uhttps://www.sciencedirect.com/science/article/pii/S02085216210014790 v423 a<p>The excessive drinking of alcohol can disrupt the neural system. This can be observed by properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal of complex nature. Therefore, an accurate categorization between alcoholic (A) and non-alcoholic (NA) subjects, while using a short time EEG recording, is a challenging task. In this paper a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory modes, and machine learning algorithms is devised for an effective identification of alcoholism. The Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used to respectively decompose the considered EEG signals in Intrinsic Mode Functions (IMFs) and Modes. Onward, the SODPs, derived from first six IMFs and Modes, are considered. Features of SODPs are mined. To reduce the dimension of features set and computational complexity of the classification model, the pertinent features selection is made on the basis of Wilcoxon statistical test. Three features with p-values (p) of &lt; 0.05 are selected from each intended SODP and these are the Central Tendency Measure (CTM), area and mean. These features are used for the discrimination between A and NA classes. In order to determine a suitable EEG signal segment length for the intended application, experiments are performed by considering features extracted from three different length time windows. The classification is carried out by using the Least Square Support Vector Machine (LS-SVM), Multilayer perceptron neural network (MLPNN), K-Nearest Neighbour (KNN) and Random Forest (RF) algorithms. The applicability is tested by using the UCI-KDD EEG dataset. The results are noteworthy for MLPNN with 99.89% and 99.45% accuracies for EMD and VMD respectively for 8-second window.</p>
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