Alcoholic EEG signals recognition based on phase space dynamic and geometrical features

Alcoholism is a severe disorder that leads to brain problems and associated cognitive, emotional and behavioral impairments. This disorder is typically diagnosed by a questionnaire technique known as CAGE that measures the severity of a drinking problem. This is a time-consuming, onerous, error-prone, and biased method. Hence, this article aims to establish a novel framework for automatic detecting alcoholism using electroencephalogram (EEG) signals, which can mitigate these issues and help clinicians and researchers. In the proposed framework, firstly, we explore the phase space dynamic of EEG signals for visualizing the chaotic nature and complexity of EEG signals. Secondly, we discover thirty-four graphical features for decoding the chaotic behavior of normal and alcoholic EEG signals. After that, we investigate thirteen feature selection in combination with eleven machine learning and neural network classifiers to select the best combination for the development of an efficient framework. The experimental results reveal that the proposed method achieves the highest classification performance involving 99.16% accuracy, 100% sensitivity and 98.36% specificity for the twenty-three features selected by Henry gas solubility optimization with feedforward neural network (FFNN). The proposed system provides a new visual biomarker for alcoholic detection. In addition, we developed two new indexes using clinically relevant features to distinguish normal and alcoholic classes with a single number. These indexes can help medical teams, commercial users as well as product developers to develop a real-time system.