Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals.

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ID: 85531
2019
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Abstract
Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.
Reference Key
ay2019automatedjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ay, Betul;Yildirim, Ozal;Talo, Muhammed;Baloglu, Ulas Baran;Aydin, Galip;Puthankattil, Subha D;Acharya, U Rajendra;
Journal Journal of medical systems
Year 2019
DOI
10.1007/s10916-019-1345-y
URL
Keywords
Deep learning cnn-lstm depression detection eeg signals hybrid deep models

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