EEG-based mild depression recognition using convolutional neural network.
Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided de...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 6; pp. 1341 - 1353 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136505531&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136505531 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2019 vid: 57 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136505531 136505531 NLM30778842 136505531 10.1007/s11517-019-01959-2 NLM30778842 136505531 ppf: 1341 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: EEG-based mild depression recognition using convolutional neural network. aug: au: Li, Xiaowei La, Rong Wang, Ying Niu, Junhong Zeng, Shuai Sun, Shuting Zhu, Jing affil: Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China sug: subj: Neural Networks (Computer) Electroencephalography Depression Diagnosis Adolescence Electrodes Case Control Studies Algorithms Female Time Factors Image Processing, Computer Assisted Young Adult Emotions Diagnosis, Computer Assisted Male Human Adolescent: 13-18 years Female Male ab: Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression. Graphical abstract The EEG power of theta, alpha, and beta bands is calculated separately under trial-wise and frame-wise strategies and is organized into three input forms of deep neural networks: feature vector, images without electrode location (spatial information), and images with electrode location. The role of EEG's spectral and spatial information in mild depression recognition is investigated through ConvNet, and the role of EEG's temporal information is investigated using different architectures to aggregate temporal features from multiple frames. The ConvNet and models for aggregating temporal features are transferred from the state-of-the-art model in mental load classification. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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