Deep-Asymmetry : Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 20(2020), 22 vom: 15. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kang, Min [VerfasserIn] |
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| Links: |
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| Themen: |
Asymmetry |
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| Anmerkungen: |
Date Completed 06.04.2021 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.3390/s20226526 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM317703404 |
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| 500 | |a Date Revised 30.03.2024 | ||
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Letter | |
| 650 | 4 | |a asymmetry | |
| 650 | 4 | |a asymmetry image | |
| 650 | 4 | |a convolutional neural networks | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a electroencephalogram | |
| 650 | 4 | |a major depressive disorder | |
| 700 | 1 | |a Kwon, Hyunjin |e verfasserin |4 aut | |
| 700 | 1 | |a Park, Jin-Hyeok |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Seokhwan |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Youngho |e verfasserin |4 aut | |
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