Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals
Emotional decoding and automatic identification of major depressive disorder (MDD) is helpful to doctors in diagnosis of the disease on time, and electroencephalogram (EEG) is sensitive to the changes of functional state of human brain, showing its potential to help to diagnose MDD. In this paper, a...
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| Published in | Frontiers in human neuroscience Vol. 14; p. 284 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Lausanne
Frontiers Research Foundation
23.09.2020
Frontiers Media S.A |
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| Online Access | Get full text |
| ISSN | 1662-5161 1662-5161 |
| DOI | 10.3389/fnhum.2020.00284 |
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| Abstract | Emotional decoding and automatic identification of major depressive disorder (MDD) is helpful to doctors in diagnosis of the disease on time, and electroencephalogram (EEG) is sensitive to the changes of functional state of human brain, showing its potential to help to diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross correlation with EEG signals is proposed and tested on 32 subjects (16 MDD and 16 healthy controls (HC)). First, the structure feature and connectivity feature of theta, alpha and beta band are extracted on the preprocessed and segmented EEG. Second, the structure feature matrix of theta, alpha and beta are added to and subtracted the connectivity feature matrix respectively to obtain the mixed features. Finally, the structure feature, connectivity feature and the mixed features are fed to six classifiers respectively to select the suitable features for the classification, and it is found that we have the best classification results using the mixed features. The results are also compared with those from some of the state-of-the-art methods, and we achieve accuracy of 94.13%, sensitivity of 95.74%, specificity of 93.52% and f1_score of 95.62% on the data from the Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a Brain–computer interfacing(BCI) system that may help for clinical purposes. |
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| AbstractList | Emotional decoding and automatic identification of major depressive disorder (MDD) is helpful to doctors in diagnosis of the disease on time, and electroencephalogram (EEG) is sensitive to the changes of functional state of human brain, showing its potential to help to diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross correlation with EEG signals is proposed and tested on 32 subjects (16 MDD and 16 healthy controls (HC)). First, the structure feature and connectivity feature of theta, alpha and beta band are extracted on the preprocessed and segmented EEG. Second, the structure feature matrix of theta, alpha and beta are added to and subtracted the connectivity feature matrix respectively to obtain the mixed features. Finally, the structure feature, connectivity feature and the mixed features are fed to six classifiers respectively to select the suitable features for the classification, and it is found that we have the best classification results using the mixed features. The results are also compared with those from some of the state-of-the-art methods, and we achieve accuracy of 94.13%, sensitivity of 95.74%, specificity of 93.52% and f1_score of 95.62% on the data from the Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a Brain–computer interfacing(BCI) system that may help for clinical purposes. Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes. Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes. |
| Author | Duan, Lijuan Wang, Changming Huang, Juan Qiao, Yuanhua Sha, Sha Zhang, Xiaolong Huang, Xiaohan Duan, Huifeng Qi, Shunai |
| AuthorAffiliation | 1 Faculty of Information Technology, Beijing University of Technology , Beijing , China 5 Beijing Anding Hospital, Capital Medical University , Beijing , China 8 Department of Neurosurgery, Xuanwu Hospital, Capitap Medical University , Beijing , China 3 National Engineering Laboratory for Critical Technologies of Information Security Classified Protection , Beijing , China 4 College of Applied Sciences, Beijing University of Technology , Beijing , China 6 Advanced Innovation Center for Human Brain Protection, Capital Medical University , Beijing , China 2 Beijing Key Laboratory of Trusted Computing , Beijing , China 7 Brain-inspired Intelligence and Clinical Translational Research Center, Xuanwu Hospital, Capitap Medical University , Beijing , China |
| AuthorAffiliation_xml | – name: 5 Beijing Anding Hospital, Capital Medical University , Beijing , China – name: 6 Advanced Innovation Center for Human Brain Protection, Capital Medical University , Beijing , China – name: 7 Brain-inspired Intelligence and Clinical Translational Research Center, Xuanwu Hospital, Capitap Medical University , Beijing , China – name: 3 National Engineering Laboratory for Critical Technologies of Information Security Classified Protection , Beijing , China – name: 1 Faculty of Information Technology, Beijing University of Technology , Beijing , China – name: 4 College of Applied Sciences, Beijing University of Technology , Beijing , China – name: 2 Beijing Key Laboratory of Trusted Computing , Beijing , China – name: 8 Department of Neurosurgery, Xuanwu Hospital, Capitap Medical University , Beijing , China |
| Author_xml | – sequence: 1 givenname: Lijuan surname: Duan fullname: Duan, Lijuan – sequence: 2 givenname: Huifeng surname: Duan fullname: Duan, Huifeng – sequence: 3 givenname: Yuanhua surname: Qiao fullname: Qiao, Yuanhua – sequence: 4 givenname: Sha surname: Sha fullname: Sha, Sha – sequence: 5 givenname: Shunai surname: Qi fullname: Qi, Shunai – sequence: 6 givenname: Xiaolong surname: Zhang fullname: Zhang, Xiaolong – sequence: 7 givenname: Juan surname: Huang fullname: Huang, Juan – sequence: 8 givenname: Xiaohan surname: Huang fullname: Huang, Xiaohan – sequence: 9 givenname: Changming surname: Wang fullname: Wang, Changming |
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| SubjectTerms | Accuracy Artificial intelligence Asymmetry Brain Cerebral hemispheres Classification cross correlation EEG Electroconvulsive therapy Electrodes Electroencephalography feature Human Neuroscience interhemispheric asymmetry Learning algorithms Machine learning major depressive disorder (MDD) Medical research Mental depression Mental disorders Neural networks Time series Wavelet transforms |
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| Title | Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals |
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