Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression
DC Field | Value | Language |
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dc.contributor.author | Kang, Min | - |
dc.contributor.author | Kwon, Hyunjin | - |
dc.contributor.author | Park, Jin-Hyeok | - |
dc.contributor.author | Kang, Seokhwan | - |
dc.contributor.author | Lee, Youngho | - |
dc.date.available | 2020-12-16T01:40:48Z | - |
dc.date.created | 2020-11-20 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79356 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | Sensors (Switzerland) | - |
dc.title | Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000595045800001 | - |
dc.identifier.doi | 10.3390/s20226526 | - |
dc.identifier.bibliographicCitation | Sensors (Switzerland), v.20, no.22, pp.1 - 12 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85096035003 | - |
dc.citation.endPage | 12 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | Sensors (Switzerland) | - |
dc.citation.volume | 20 | - |
dc.citation.number | 22 | - |
dc.contributor.affiliatedAuthor | Kang, Min | - |
dc.contributor.affiliatedAuthor | Kwon, Hyunjin | - |
dc.contributor.affiliatedAuthor | Park, Jin-Hyeok | - |
dc.contributor.affiliatedAuthor | Kang, Seokhwan | - |
dc.contributor.affiliatedAuthor | Lee, Youngho | - |
dc.type.docType | Letter | - |
dc.subject.keywordAuthor | Asymmetry | - |
dc.subject.keywordAuthor | Asymmetry image | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Electroencephalogram | - |
dc.subject.keywordAuthor | Major depressive disorder | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Data visualization | - |
dc.subject.keywordPlus | Electroencephalography | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Matrix algebra | - |
dc.subject.keywordPlus | 2D images | - |
dc.subject.keywordPlus | Eeg datum | - |
dc.subject.keywordPlus | Effective tool | - |
dc.subject.keywordPlus | Electro-encephalogram (EEG) | - |
dc.subject.keywordPlus | Learning methods | - |
dc.subject.keywordPlus | On-machines | - |
dc.subject.keywordPlus | Short time Fourier transforms | - |
dc.subject.keywordPlus | Visualization research | - |
dc.subject.keywordPlus | Deep learning | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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