Processing EEG signals alcohol-addicted patients using neural networks

Alcoholism is one of the most common diseases, which creates problems for contemporary society. To date, there are still no standardized methods and tests to detect this disease. This study presents a new methodology based on an integrated approach to the study of Electroencephalography (EEG) signals, using a combination of nonlinear dynamics and machine learning methods. We applied signal encoding in the form of a 16-channel image, which was fed into the ResNet20 and GoogLeNet models for training. Wavelet transforms and fast Fourier transforms were considered for data preprocessing. In addition to the application of neural networks, the study of EEG signals was carried out by computing Lyapunov exponents and multiscale entropy. The question of choosing the most effective maternal wavelet was previously investigated. The wavelet transformations of Morlet, Meyer, Mexican Hat, Daubechies, and Gauss were studied. A spectrum of five Lyapunov exponents was calculated using the Sano-Savada method. To confirm the reliability of the results, the senior Lyapunov exponent was previously calculated by three more methods: Wolf, Kantz, Rosenstein. Previously, the methods were tested on such classical problems as the hyperchaotic Hénon map, logistic map, Rössler attractor and Lorenz attractor. The use of several approaches for the analysis of EEG signals confirmed the reliability of the results. The study demonstrates that the GoogLeNet model is more efficient than ResNet50. The experiment results showed that, based on the developed methodology, the GoogLeNet model is more efficient than ResNet50. The results of the experiment show that the wavelet transform, compared to the Fourier transform, gives higher accuracy for the analysis of EEG signals of patients with alcoholism.