A hydrid deep neural network for electroencephalogram (EEG)-based screening of depression

Abstract

Technological development is a major contributor to improve people's quality of life. In recent times happy life has been considered one of the major requirements as people live under stress and face several mental disorders like depression, anxiety, and loneliness. In the mental disorder space, depression is a major and common disease. According to the World Health Organization (WHO), it is estimated that 5% of adults suffer from depression. Diagnosis of depression has several challenges, for example, patient counseling is time consuming, over-dependence on doctors and accuracy of diagnosis. To resolve these diagnosis issues, computer aided system is required with the use of machine learning tools. The objective of this research to develop hybrid deep learning model by using CNN and LSTM. The dataset used in this study contains 945 subjects of mental disorders and healthy control subjects. Three hybrid models were developed and compared with different sets of extracted features. Raw data was pre-processed and applied in hybrid model, and at the end the model was validated with the unknown EEG dataset. The hybrid model with entire features of dataset reported an accuracy of 98.0% and performed better in comparison with other two models which were trained with extracted features by using decision tree classifier. The results show that the developed hybrid CNN and LSTM model is accurate, less complex, and useful in detecting mental disorders including depression using EEG signals.