A Cognitive Framework to detect AUD patients from EEG signal using Hybrid Super Learning model

Abstract

No need to clarify the fact that consuming alcohol has some serious effect on the human brain and hampers our daily lifestyle. Also, it may cause difficulties in recalling memories, instability, and even blackout. Recognizing early symptoms of substance dependence and having adequate care in the rehabilitation phase will make a big difference. The screening test for patients' alcohol dependence was arbitrary and could misinterpret the true level of alcohol consumption in some cases. Although the paradigm of neuroimaging (EEG) showed positive outcomes of research in obtaining objective findings when assessing and diagnosing intoxicated patients. This work extract features from EEG brain signals and then optimizes the collection of features using mutual information, feature importance, LASSO regularization, and the RFE method step by step. The optimized features set is then considered for detecting AUD (Alcohol Use Disorder) patients and healthy persons. Super Learning approaches adopted for the classification task. This is accomplished by bagging and boosting results from a set of machine learning models for classification. The findings reveal that the ensemble method of feature optimization accompanied by the hybrid super-learning classification provides better performance. The proposed approach has experimented with EEG data set from the UCI Machine Learning repository and the experimental results substantiate the efficacy of the approach and also comparable to the state-of-the-art approaches.