Abstract
It is of utmost importance to take care of mental health. This is especially true in the ongoing COVID-19 pandemic. Depression is a crippling problem and must be treated quickly. It is useful to focus efforts on new, efficient, and accurate methods of depression detection. We intend to detect whether a person is in a depressed state of mind. We will be using deep learning models. The algorithms that have been used are feed-forward deep neural network (FNN), long short-term memory (LSTM) neural network, simple recurrent neural network (RNN), 1D convolutional neural network (1D CNN), and gated recurrent units (GRU) neural network. These models have been trained by utilizing electroencephalogram (EEG) sensor datasets. They classify individuals into either “depressed” or “non-depressed” categories. These models have been compared using confusion matrix, precision, recall score, F1 score, and accuracy. The best-performing models identified are FNN and 1D CNN.
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Gopichand, G., Ramesh, A., Tholappa, V., Sridara Pandian, G. (2023). Depression Detection Using Deep Learning. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_20
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DOI: https://doi.org/10.1007/978-981-99-6706-3_20
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