A Pervasive Approach to EEG-Based Depression Detection
Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed pat...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5238028 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564208874553344 |
|---|---|
| author | Hanshu Cai Jiashuo Han Yunfei Chen Xiaocong Sha Ziyang Wang Bin Hu Jing Yang Lei Feng Zhijie Ding Yiqiang Chen Jürg Gutknecht |
| author_facet | Hanshu Cai Jiashuo Han Yunfei Chen Xiaocong Sha Ziyang Wang Bin Hu Jing Yang Lei Feng Zhijie Ding Yiqiang Chen Jürg Gutknecht |
| author_sort | Hanshu Cai |
| collection | DOAJ |
| description | Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis. |
| format | Article |
| id | doaj-art-81d6db0a394d4d4bb2f639a55b0e7a1e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-81d6db0a394d4d4bb2f639a55b0e7a1e2025-02-03T01:11:37ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/52380285238028A Pervasive Approach to EEG-Based Depression DetectionHanshu Cai0Jiashuo Han1Yunfei Chen2Xiaocong Sha3Ziyang Wang4Bin Hu5Jing Yang6Lei Feng7Zhijie Ding8Yiqiang Chen9Jürg Gutknecht10Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaDepartment of Child Psychology, Lanzhou University Second Hospital, Lanzhou, ChinaBeijing Anding Hospital, Capital Medical University, Beijing, ChinaThe Third People’s Hospital of Tianshui City, Tianshui, ChinaInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaComputer Systems Institute, ETH Zürich, Zürich, SwitzerlandNowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.http://dx.doi.org/10.1155/2018/5238028 |
| spellingShingle | Hanshu Cai Jiashuo Han Yunfei Chen Xiaocong Sha Ziyang Wang Bin Hu Jing Yang Lei Feng Zhijie Ding Yiqiang Chen Jürg Gutknecht A Pervasive Approach to EEG-Based Depression Detection Complexity |
| title | A Pervasive Approach to EEG-Based Depression Detection |
| title_full | A Pervasive Approach to EEG-Based Depression Detection |
| title_fullStr | A Pervasive Approach to EEG-Based Depression Detection |
| title_full_unstemmed | A Pervasive Approach to EEG-Based Depression Detection |
| title_short | A Pervasive Approach to EEG-Based Depression Detection |
| title_sort | pervasive approach to eeg based depression detection |
| url | http://dx.doi.org/10.1155/2018/5238028 |
| work_keys_str_mv | AT hanshucai apervasiveapproachtoeegbaseddepressiondetection AT jiashuohan apervasiveapproachtoeegbaseddepressiondetection AT yunfeichen apervasiveapproachtoeegbaseddepressiondetection AT xiaocongsha apervasiveapproachtoeegbaseddepressiondetection AT ziyangwang apervasiveapproachtoeegbaseddepressiondetection AT binhu apervasiveapproachtoeegbaseddepressiondetection AT jingyang apervasiveapproachtoeegbaseddepressiondetection AT leifeng apervasiveapproachtoeegbaseddepressiondetection AT zhijieding apervasiveapproachtoeegbaseddepressiondetection AT yiqiangchen apervasiveapproachtoeegbaseddepressiondetection AT jurggutknecht apervasiveapproachtoeegbaseddepressiondetection AT hanshucai pervasiveapproachtoeegbaseddepressiondetection AT jiashuohan pervasiveapproachtoeegbaseddepressiondetection AT yunfeichen pervasiveapproachtoeegbaseddepressiondetection AT xiaocongsha pervasiveapproachtoeegbaseddepressiondetection AT ziyangwang pervasiveapproachtoeegbaseddepressiondetection AT binhu pervasiveapproachtoeegbaseddepressiondetection AT jingyang pervasiveapproachtoeegbaseddepressiondetection AT leifeng pervasiveapproachtoeegbaseddepressiondetection AT zhijieding pervasiveapproachtoeegbaseddepressiondetection AT yiqiangchen pervasiveapproachtoeegbaseddepressiondetection AT jurggutknecht pervasiveapproachtoeegbaseddepressiondetection |