Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions
Background: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2023-07-01
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| Series: | Journal of Integrative Neuroscience |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/JIN/22/4/10.31083/j.jin2204093 |
| _version_ | 1797768960421134336 |
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| author | Jianli Yang Zhen Zhang Peng Xiong Xiuling Liu |
| author_facet | Jianli Yang Zhen Zhang Peng Xiong Xiuling Liu |
| author_sort | Jianli Yang |
| collection | DOAJ |
| description | Background: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. Methods: To solve the aforementioned problems, nonlinear feature Lempel–Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. Results: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. Conclusions: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment. |
| first_indexed | 2024-03-12T21:00:57Z |
| format | Article |
| id | doaj.art-575691d72f6846279f74e4783be68a52 |
| institution | Directory Open Access Journal |
| issn | 0219-6352 |
| language | English |
| last_indexed | 2024-03-12T21:00:57Z |
| publishDate | 2023-07-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Journal of Integrative Neuroscience |
| spelling | doaj.art-575691d72f6846279f74e4783be68a522023-07-31T07:12:11ZengIMR PressJournal of Integrative Neuroscience0219-63522023-07-012249310.31083/j.jin2204093S0219-6352(22)00476-4Depression Detection Based on Analysis of EEG Signals in Multi Brain RegionsJianli Yang0Zhen Zhang1Peng Xiong2Xiuling Liu3College of Electronic Information and Engineering, Hebei University, 071002 Baoding, Hebei, ChinaCollege of Electronic Information and Engineering, Hebei University, 071002 Baoding, Hebei, ChinaCollege of Electronic Information and Engineering, Hebei University, 071002 Baoding, Hebei, ChinaCollege of Electronic Information and Engineering, Hebei University, 071002 Baoding, Hebei, ChinaBackground: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. Methods: To solve the aforementioned problems, nonlinear feature Lempel–Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. Results: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. Conclusions: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.https://www.imrpress.com/journal/JIN/22/4/10.31083/j.jin2204093depressioneegfeature extractionbrain region combination |
| spellingShingle | Jianli Yang Zhen Zhang Peng Xiong Xiuling Liu Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions Journal of Integrative Neuroscience depression eeg feature extraction brain region combination |
| title | Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions |
| title_full | Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions |
| title_fullStr | Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions |
| title_full_unstemmed | Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions |
| title_short | Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions |
| title_sort | depression detection based on analysis of eeg signals in multi brain regions |
| topic | depression eeg feature extraction brain region combination |
| url | https://www.imrpress.com/journal/JIN/22/4/10.31083/j.jin2204093 |
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