Analysis of Alcoholic EEG Signal using Semantic Technologies

Alcohol is a depressant drug that interferes with the functions of the central nervous system. It affects brain functions and results in mental and behavioural problems in people. Alcoholism is brought on by excessive alcohol consumption. By paralysing the centres that regulate the respiratory and circulatory systems, it may occasionally result in death.Due to the disease's consequences on individuals and added financial burden to our society, alcoholism diagnosis is crucial. There are also not many trustworthy standard test methods for alcoholism identification in addition to all of the above. This research study has opted to develop a new method to detect alcoholism by analysing Electroencephalogram (EEG) data as an improvement to the existing ways and to help its utilisation in medical practises. The strategy combines deep learning and machine learning techniques. For feature extraction, the CNN model is employed initially, then the SVM with cross validation machine learning model for classification.Finally, the proposed model could achieve an accuracy of 96.01% for deep learning model and 98.21% for machine learning model by comparing with various models.