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

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

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.

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Authors and Affiliations

  1. Department of Computer Engineering, Fırat University, Elazığ, Turkey

    Betul Ay & Galip Aydin

  2. Department of Computer Engineering, Munzur University, Tunceli, Turkey

    Ozal Yildirim, Muhammed Talo & Ulas Baran Baloglu

  3. Department of Electrical Engineering, National Institute of Technology Calicut, Calicut, India

    Subha D. Puthankattil

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  5. Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  6. School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, 47500, Subang Jaya, Malaysia

    U. Rajendra Acharya

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  1. Betul Ay
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  2. Ozal Yildirim
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Correspondence to Ozal Yildirim.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We have obtained the ethical approval for depression data from Medical College Calicut, Kerala, India.

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Ay, B., Yildirim, O., Talo, M. et al. Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals. J Med Syst 43, 205 (2019). https://doi.org/10.1007/s10916-019-1345-y

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