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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Veröffentlicht in:	Journal of medical systems Jg. 43; H. 7; S. 205 - 12
Hauptverfasser:	Ay, Betul, Yildirim, Ozal, Talo, Muhammed, Baloglu, Ulas Baran, Aydin, Galip, Puthankattil, Subha D., Acharya, U. Rajendra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2019 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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