Computer-aided diagnosis of depression using EEG signals
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depres...
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| Format: | Article |
| Language: | English |
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KARGER, ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND
2015
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/15746/1/Computer-Aided_Diagnosis_of_Depression_Using_EEG_Signals.pdf |
| _version_ | 1796946953064415232 |
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| author | Acharya, U.R. Sudarshan, V.K. Adeli, H. Santhosh, J. Koh, J.E.W. Adeli, A. |
| author_facet | Acharya, U.R. Sudarshan, V.K. Adeli, H. Santhosh, J. Koh, J.E.W. Adeli, A. |
| author_sort | Acharya, U.R. |
| collection | UM |
| description | The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression. (C) 2015 S. Karger AG, Basel |
| first_indexed | 2024-03-06T05:39:33Z |
| format | Article |
| id | um.eprints-15746 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:39:33Z |
| publishDate | 2015 |
| publisher | KARGER, ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND |
| record_format | dspace |
| spelling | um.eprints-157462016-04-08T02:26:46Z http://eprints.um.edu.my/15746/ Computer-aided diagnosis of depression using EEG signals Acharya, U.R. Sudarshan, V.K. Adeli, H. Santhosh, J. Koh, J.E.W. Adeli, A. T Technology (General) TA Engineering (General). Civil engineering (General) The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression. (C) 2015 S. Karger AG, Basel KARGER, ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND 2015 Article PeerReviewed application/pdf en http://eprints.um.edu.my/15746/1/Computer-Aided_Diagnosis_of_Depression_Using_EEG_Signals.pdf Acharya, U.R. and Sudarshan, V.K. and Adeli, H. and Santhosh, J. and Koh, J.E.W. and Adeli, A. (2015) Computer-aided diagnosis of depression using EEG signals. European Neurology, 73 (5-6). pp. 329-336. ISSN 0014-3022 , DOI https://doi.org/10.1159/000381950 <https://doi.org/10.1159/000381950>. http://www.ncbi.nlm.nih.gov/pubmed/25997732 10.1159/000381950 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Acharya, U.R. Sudarshan, V.K. Adeli, H. Santhosh, J. Koh, J.E.W. Adeli, A. Computer-aided diagnosis of depression using EEG signals |
| title | Computer-aided diagnosis of depression using EEG signals |
| title_full | Computer-aided diagnosis of depression using EEG signals |
| title_fullStr | Computer-aided diagnosis of depression using EEG signals |
| title_full_unstemmed | Computer-aided diagnosis of depression using EEG signals |
| title_short | Computer-aided diagnosis of depression using EEG signals |
| title_sort | computer aided diagnosis of depression using eeg signals |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://eprints.um.edu.my/15746/1/Computer-Aided_Diagnosis_of_Depression_Using_EEG_Signals.pdf |
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