Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression
- Authors
- Kang, Min; Kwon, Hyunjin; Park, Jin-Hyeok; Kang, Seokhwan; Lee, Youngho
- Issue Date
- Nov-2020
- Publisher
- MDPI AG
- Keywords
- Asymmetry; Asymmetry image; Convolutional neural networks; Deep learning; Electroencephalogram; Major depressive disorder
- Citation
- Sensors (Switzerland), v.20, no.22, pp.1 - 12
- Journal Title
- Sensors (Switzerland)
- Volume
- 20
- Number
- 22
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79356
- DOI
- 10.3390/s20226526
- ISSN
- 1424-8220
- Abstract
- To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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