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Medical & Biological Engineering & Computing

Erschienen in:

19.02.2019 | Original Article

EEG-based mild depression recognition using convolutional neural network

verfasst von: Xiaowei Li, Rong La, Ying Wang, Junhong Niu, Shuai Zeng, Shuting Sun, Jing Zhu

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 6/2019

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Abstract

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.

Vorheriger Artikel Grasshopper optimization algorithm–based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals

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Titel: EEG-based mild depression recognition using convolutional neural network
verfasst von: Xiaowei Li
Rong La
Ying Wang
Junhong Niu
Shuai Zeng
Shuting Sun
Jing Zhu
Publikationsdatum: 19.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 6/2019
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-019-01959-2

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