Innovative and Effective Machine Learning-Based Method to Analyze Alcoholic Brain Activity with Nonlinear Dynamics and Electroencephalography Data

Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve over time. This study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. Approximate entropy (ApEn), sample entropy (SampEn), Shannon and Renyi entropies, permutation entropy (PE), Tsalli’s entropy (TS), fuzzy entropy (FE), wavelet entropy (WE), and Kolmogorov–Sinai entropy (KSE) are some of the features that can be extracted. T test, Wilcoxon, and Bhattacharyya were among the many ranking methods used to order the features that had been extracted. Under the premise of feature optimization, the EEG signals are evaluated and classified using the support vector classifier as either alcoholic or non-alcoholic. This means the proposed method is particularly well-suited to classify EEG signals. Here, a CEHOC (Chaotic Elephant Herding Optimization-based Classification) is applied to categorize the massive extent of assorted datasets to upsurge the enactment of disseminated computing. The findings demonstrate the superior efficiency of the CEHOC algorithm. The SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method and CEHOC optimization achieves classification accuracy of 95.89%, sensitivity of 94.43%, and specificity of 96.67%. This system can serve as a decision support tool for doctors making alcoholism diagnoses because it is quick, accurate, and cheap. Rehabilitation centers can benefit greatly from long-term assessments of alcoholics to track the effectiveness of interventions designed to mitigate or reverse brain damage.