Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, pract...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8639 |
| _version_ | 1797572277124988928 |
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| author | Yanting Xu Hongyang Zhong Shangyan Ying Wei Liu Guibin Chen Xiaodong Luo Gang Li |
| author_facet | Yanting Xu Hongyang Zhong Shangyan Ying Wei Liu Guibin Chen Xiaodong Luo Gang Li |
| author_sort | Yanting Xu |
| collection | DOAJ |
| description | Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8–30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD. |
| first_indexed | 2024-03-10T20:53:56Z |
| format | Article |
| id | doaj.art-a9a06ef1687241d69afe82fad4f65b8f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:53:56Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a9a06ef1687241d69afe82fad4f65b8f2023-11-19T18:06:01ZengMDPI AGSensors1424-82202023-10-012320863910.3390/s23208639Depressive Disorder Recognition Based on Frontal EEG Signals and Deep LearningYanting Xu0Hongyang Zhong1Shangyan Ying2Wei Liu3Guibin Chen4Xiaodong Luo5Gang Li6College of Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, ChinaThe Second Hospital of Jinhua, Jinhua 321016, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaDepressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8–30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.https://www.mdpi.com/1424-8220/23/20/8639depressive disorder (DD)electroencephalogram (EEG)beta rhythmconvolutional neural network (CNN)long short-term memory (LSTM)deep learning |
| spellingShingle | Yanting Xu Hongyang Zhong Shangyan Ying Wei Liu Guibin Chen Xiaodong Luo Gang Li Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning Sensors depressive disorder (DD) electroencephalogram (EEG) beta rhythm convolutional neural network (CNN) long short-term memory (LSTM) deep learning |
| title | Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning |
| title_full | Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning |
| title_fullStr | Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning |
| title_full_unstemmed | Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning |
| title_short | Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning |
| title_sort | depressive disorder recognition based on frontal eeg signals and deep learning |
| topic | depressive disorder (DD) electroencephalogram (EEG) beta rhythm convolutional neural network (CNN) long short-term memory (LSTM) deep learning |
| url | https://www.mdpi.com/1424-8220/23/20/8639 |
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