Early Detection of Depression and Alcoholism Disorders by EEG Signal

The World Health Organization reported that more than 264 and 80 million patients worldwide suffer from depression and alcoholism, respectively. Depression and alcoholism might cause severe negative repercussions on a patient’s life and relationships, such as self-harm and suicide. A person can lead a normal life after these brain disorders are timely and accurately diagnosed and cured. In order to recognize the brain’s activity and identify different mental disorders, Electroencephalography (EEG) is often employed. The EEG signals in our study are separated into rhythms in the empirical wavelet transform domain, and then linear and nonlinear features are extracted. Significant features are selected by a feature selection method, and the output of the feature selection method is fed into a classifier. In this paper, a fast and effective diagnostic tool is proposed to detect and recognize depression and alcoholism disorders. The proposed diagnostic tool is built on the Salp Swarm Algorithm and the Tree Growth Algorithm as feature selection methods and Cascade Forward Neural Network and Feed-forward Neural Network classifiers. The diagnostic tool is evaluated on two datasets for depression and alcoholism, and the results show that the classification accuracies are 100% and 99.58% for depression and alcoholism, using 10-fold cross-validation strategy, respectively. The proposed diagnostic tool can be used in hospitals and clinics for fast and accurate detection of depression and alcoholism. In addition, we introduce a novel depression diagnostic index and alcoholism diagnostic index, which can be used as biomarkers for healthcare provider to diagnose depression and alcoholism without using machine learning approaches.