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

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 t...

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Vydáno v:	Journal of medical systems Ročník 43; číslo 7; s. 205 - 12
Hlavní autoři:	Ay, Betul, Yildirim, Ozal, Talo, Muhammed, Baloglu, Ulas Baran, Aydin, Galip, Puthankattil, Subha D., Acharya, U. Rajendra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2019 Springer Nature B.V
Témata:	Algorithms Artificial neural networks Automation Brain Brain - physiopathology Cerebral hemispheres Deep learning Depression - diagnosis Depression - physiopathology EEG Electroencephalography Electroencephalography - methods Health Informatics Health Informatics and Computer Vision Health Sciences Hemispheric laterality Humans Image & Signal Processing Image Processing, Computer-Assisted Learning Long short-term memory Medicine Medicine & Public Health Mental depression Mood Neural networks Neural Networks, Computer Psychiatry Recent Advances in Deep Learning for Biomedical Signal Processing Short term Signal processing Statistics for Life Sciences Depression detection Deep learning EEG signals Hybrid deep models CNN-LSTM
ISSN:	0148-5598, 1573-689X, 1573-689X
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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.
AbstractList	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. 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. 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.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.
ArticleNumber	205
Author	Ay, Betul Yildirim, Ozal Acharya, U. Rajendra Baloglu, Ulas Baran Puthankattil, Subha D. Talo, Muhammed Aydin, Galip
Author_xml	– sequence: 1 givenname: Betul surname: Ay fullname: Ay, Betul organization: Department of Computer Engineering, Fırat University – sequence: 2 givenname: Ozal orcidid: 0000-0001-5375-3012 surname: Yildirim fullname: Yildirim, Ozal email: oyildirim@munzur.edu.tr organization: Department of Computer Engineering, Munzur University – sequence: 3 givenname: Muhammed surname: Talo fullname: Talo, Muhammed organization: Department of Computer Engineering, Munzur University – sequence: 4 givenname: Ulas Baran surname: Baloglu fullname: Baloglu, Ulas Baran organization: Department of Computer Engineering, Munzur University – sequence: 5 givenname: Galip surname: Aydin fullname: Aydin, Galip organization: Department of Computer Engineering, Fırat University – sequence: 6 givenname: Subha D. surname: Puthankattil fullname: Puthankattil, Subha D. organization: Department of Electrical Engineering, National Institute of Technology Calicut – sequence: 7 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31139932$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
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SubjectTerms	Algorithms Artificial neural networks Automation Brain Brain - physiopathology Cerebral hemispheres Deep learning Depression - diagnosis Depression - physiopathology EEG Electroencephalography Electroencephalography - methods Health Informatics Health Informatics and Computer Vision Health Sciences Hemispheric laterality Humans Image & Signal Processing Image Processing, Computer-Assisted Learning Long short-term memory Medicine Medicine & Public Health Mental depression Mood Neural networks Neural Networks, Computer Psychiatry Recent Advances in Deep Learning for Biomedical Signal Processing Short term Signal processing Statistics for Life Sciences
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Title	Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
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