DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression

• For depression detection EEG signal proves as best biomarker. • CNN-LSTM hybrid neural networks present high performance. • Large EEG dataset processing is better with deep neural network. • Windowing technique shows less computation and time complexity. Depression is a psychological disorder characterized by the continuous occurrence of bad mood state. It is critical to understand that this disorder is severely affecting people of multiple age groups across the world. This illness is now considered as a global issue and its early diagnosis will be effective in saving the lives of many people. This mental disorder can be diagnosed with the help of Electroencephalogram (EEG) signals as an analysis of these signals can indicate the prevailing mental state of the patients. This paper elaborates on the advantages of a fully automated Depression Detection System, as manual analysis of the EEG signal is very time consuming, tedious and it requires a lot of experience. This research paper presents a novel EEG based computer-aided (CAD) Hybrid Neural Network that can be identified as DepHNN (Depression Hybrid Neural Network) for depression screening. The proposed method uses Convolutional Neural Network (CNN) for temporal learning, windowing and long-short term memory (LSTM) architectures for the sequence learning process. In this model, EEG signals have been obtained from 21 drug-free, symptomatic depressed, and 24 normal patients using neuroscan. The model has less time and minimized computation complexity as it uses the windowing technique. It has attained an accuracy of 99.10% with mean absolute error (MAE) of 0.2040. The results show that the developed hybrid CNN-LSTM model is accurate, less complex, and useful in detecting depression using EEG signals.