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01-07-2019 | Mood Disorders | Image & Signal Processing

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

Authors: Betul Ay, Ozal Yildirim, Muhammed Talo, Ulas Baran Baloglu, Galip Aydin, Subha D. Puthankattil, U. Rajendra Acharya

Published in: Journal of Medical Systems | Issue 7/2019

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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Title: Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Authors: Betul Ay
Ozal Yildirim
Muhammed Talo
Ulas Baran Baloglu
Galip Aydin
Subha D. Puthankattil
U. Rajendra Acharya
Publication date: 01-07-2019
Publisher: Springer US
Keywords: Mood Disorders
Affective Disorder
Electroencephalography
Published in: Journal of Medical Systems / Issue 7/2019
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-019-1345-y

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

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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