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Abstract

Technological development is considered one of the major parts of this recent time as it helps to improve people's quality of life and resolve their issues and challenges faced in daily life. In recent times a happy life has been considered one of the major requirements for people as most people live under stress and face several mental disorders like depression, anxiety and loneliness. In the metal disorder space, depression is a major and common disease in recent society. According to the World Health Organization (WHO), it is estimated that 5% of adults suffer from depression. Diagnosis of depression has several challenges like time consuming patient counselling, over-dependence on doctors and accuracy of diagnosis. To resolve these diagnosis issues, computer aided system solution is required with the use of machine learning tool. The objective of this research is to develop hybrid deep learning model by using CNN and LSTM. The selected dataset which was used for this study contains a dataset of 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 model validated with the unknown EEG dataset. The hybrid model with entire features of dataset reported an accuracy of 98.0% and performed superior in comparison with other two models which 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.