Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals from local databases. This system was optimized through a series of experiments to identify the most accurate model. The experiments employed a pre-trained convolutional neural network, ResNet18, fine-tuned on time-frequency synchrosqueezed wavelet transform (SSWT) images derived from EEG signals. Various data augmentation methods, including image processing techniques and noises, were applied to identify the best model for CCADD. To offer this device with minimal electrodes, we aimed to balance high accuracy with fewer electrodes. Two publicly databases were evaluated using this approach. Dataset I included 31 individuals detected with major depressive disorder and a control class of 27 age-matched healthy subjects. Dataset II comprised 90 participants, with 45 diagnosed with depression and 45 healthy controls. The leave-subjects-out cross-validation method with 20 subjects was used to validate the proposed method. The highest average accuracies for the selected model are 98%, 97%, 91%, and 88% for the parietal and central lobes in Databases I and II, respectively. The corresponding highest f-scores are 96.27%, 94.87%, 90.56%, and 89.65%. The highest intra-database accuracy and F1-score are 75.10% and 73.56% when training with SSWT images from Database II and testing with parietal images from Database I. This study introduces a novel cloud-based model for depression detection, paving the way for effective diagnostic tools and potentially revolutionizing depression management.