Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression
Open accessAbstract
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.
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The Asymmetry Matrix Image (AMI) outperforms traditional methods for classifying depression patients versus healthy controls.
“AMI has been shown to outperform traditional methods, delivering superior classification accuracy and enhancing the distinction between depression patients and healthy controls.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [127]
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- Authors
- Kang, M., Kwon, H., Park, J.-H., Kang, S., & Lee, Y.
- Affiliations
- Gachon University (KR)
- Department of IT Convergence Engineering, Gachon University, Sungnam-si 13306, Korea
- Corresponding author
- Lee, Youngho
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-11-15
- Volume
- 20
- Issue
- 22
- Pages
- 6526
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7696521
- OpenAlex
- W3105250175
- Cited by
- 71
- References
- 30
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 13 · 2023: 16 · 2022: 9 · 2021: 6
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Institute for Information and Communications Technology Promotion (IITP-2020-2017-0-01630)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- major depressive disorder, EEG asymmetry, EEG visualization, alpha band, convolutional neural networks, EEG biomarkers
- MeSH terms
- Deep Learning, Major Depressive Disorder, Humans, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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