変分オートエンコーダを用いた睡眠時脳波の特徴抽出に関する研究
変分オートエンコーダを用いた睡眠時脳波の特徴抽出に関する研究
カテゴリ: 論文誌(論文単位)
グループ名: 【C】電子・情報・システム部門
発行日: 2023/04/01
タイトル(英語): A Study on Variational AutoEncoder to Extract Characteristic Patterns from Electroencephalograms During Sleep
著者名: 杉江 倫太朗(福井大学大学院工学研究科知識社会基礎工学専攻),高田 宗樹(福井大学大学院工学研究科知識社会基礎工学専攻),中山 明峰(名古屋市立大学睡眠医療センター),岡崎 涼(名古屋市立大学睡眠医療センター)
著者名(英語): Rintaro Sugie (Department of Fundamental Engineering for Knowledge-Based Society, Graduate School of Engineering, University of Fukui), Hiroki Takada (Department of Fundamental Engineering for Knowledge-Based Society, Graduate School of Engineering, University of Fukui), Meiho Nakayama (Good Sleep Center, Nagoya City University), Ryo Okazaki (Good Sleep Center, Nagoya City University)
キーワード: 変分オートエンコーダ,深層学習,回帰型ニューラルネットワーク,終夜睡眠ポリグラフ検査,睡眠時脳波,メニエール病 variational autoencoder,deep learning,recurrent neural network,polysomnography,electroencephalograms during sleep,meniere's disease
要約(英語): Meniere’s disease, a type of inner ear disease, is thought to be caused by ischemic lesions in the inner ear. On the other hand, Meniere’s disease is often associated with sleep apnea syndrome, and the relationship between the two has been pointed out. In recent years, many patients with Meniere’s disease have shown improvement in their symptoms after discontinuation or suppression of medication and sleep therapy. In this study, we hypothesized that the Electroencephalogram (EEG) during sleep in patients with Meniere’s disease has a characteristic pattern that is not seen in normal subjects. The EEGs of normal subjects and patients with Meniere’s disease were converted to lower dimensions using a variational auto-encoder (VAE), and the existence of characteristic differences was verified. Sub-sequence was extracted from the EEGs of 20 subjects, which was input to a variational autoencoder and was converted to lower dimensions. The machine learning was conducted for each channel. Latent variables obtained from the VAE were classified using Support Vector Machine (SVM). The results showed that the electrodes located at the back of the head had a higher correct response rate and F value.
本誌: 電気学会論文誌C(電子・情報・システム部門誌) Vol.143 No.4 (2023) 特集:医療/ヘルスケア×AI-量子・情報・エレクトロニクスの応用として
本誌掲載ページ: 510-514 p
原稿種別: 論文/日本語
電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/4/143_510/_article/-char/ja/
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