Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system

• Published in: Addiction biology Vol. 29; no. 2; pp. e13362 - n/a
• Main Authors: Zolfaghari, Sepideh, Sarbaz, Yashar, Shafiee‐Kandjani, Ali Reza
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2024; John Wiley and Sons Inc
• Subjects: Algorithms; Attention; Brain; classifiers; Cognitive ability; decision support system; Decision support systems; Discriminant analysis; EEG; electroencephalogram; Electroencephalography; Electroencephalography - methods; Eye; Humans; Information processing; Memory; Methamphetamine; Neural networks; Original; Sensorimotor integration; Short term memory; Statistical analysis; Support Vector Machine; Visual perception; wavelet; Wavelet Analysis; Wavelet transforms; classifiers; wavelet; methamphetamine; electroencephalogram; decision support system
• ISSN: 1355-6215; 1369-1600; 1369-1600
• DOI: 10.1111/adb.13362

Long‐term use of methamphetamine (meth) causes cognitive and neuropsychological impairments. Analysing the impact of this substance on the human brain can aid prevention and treatment efforts. In this study, the electroencephalogram (EEG) signals of meth abusers in the abstinence period and healthy subjects were recorded during eyes‐closed and eyes‐opened states to distinguish the brain regions that meth can significantly influence. In addition, a decision support system (DSS) was introduced as a complementary method to recognize substance users accompanied by biochemical tests. According to these goals, the recorded EEG signals were pre‐processed and decomposed into frequency bands using the discrete wavelet transform (DWT) method. For each frequency band, energy, KS entropy, Higuchi and Katz fractal dimensions of signals were calculated. Then, statistical analysis was applied to select features whose channels contain a p‐value less than 0.05. These features between two groups were compared, and the location of channels containing more features was specified as discriminative brain areas. Due to evaluating the performance of features and distinguishing the two groups in each frequency band, features were fed into a k‐nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron neural networks (MLP) and linear discriminant analysis (LDA) classifiers. The results indicated that prolonged consumption of meth has a considerable impact on the brain areas responsible for working memory, motor function, attention, visual interpretation, and speech processing. Furthermore, the best classification accuracy, almost 95.8%, was attained in the gamma band during the eyes‐closed state. In this study, the EEG signals of methamphetamine (meth) abusers in the abstinence period and healthy subjects were recorded to distinguish the brain regions that meth can significantly influence. These brain areas were discussed in terms of functionality. In addition, a decision support system (DSS) was introduced as a complementary method to recognize meth abusers accompanied by biochemical tests.