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畳み込みニューラルネットワーク に基づくうつ病の弁別
https://maebashi-it.repo.nii.ac.jp/records/407
https://maebashi-it.repo.nii.ac.jp/records/4070d254fc5-2125-4949-8ca1-a15249d28032
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
23_03 (529.6 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper_02(1) | |||||||||
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| 公開日 | 2022-03-22 | |||||||||
| タイトル | ||||||||||
| タイトル | 畳み込みニューラルネットワーク に基づくうつ病の弁別 | |||||||||
| タイトル | ||||||||||
| タイトル | A Convolutional Neural Network for Depression Discrimination | |||||||||
| 言語 | en | |||||||||
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| 言語 | jpn | |||||||||
| キーワード | ||||||||||
| 主題 | 畳み込みニューラルネットワーク | |||||||||
| キーワード | ||||||||||
| 主題 | うつ状態判別 | |||||||||
| キーワード | ||||||||||
| 主題 | 脳波 | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題 | convolutional neural network | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題 | depression discrimination | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題 | EEG | |||||||||
| 著者 |
万, 志江
× 万, 志江
× 鍾, 寧
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. I n this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patien ts. A ten fold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three category classification. |
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| 書誌情報 |
前橋工科大学研究紀要 発行日 2020-03-31 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1343-8867 | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA11225201 | |||||||||
