正規分布を用いた統計処理による呼吸性洞性不整脈の抽出精度向上

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: 2012/01/01

タイトル(英語): High Accuracy Extraction of Respiratory Sinus Arrhythmia with Statistical Processing using Normal Distribution

著者名: 沼田　崇志（東京大学大学院新領域創成科学研究科），小川　雄太郎（東京大学大学院新領域創成科学研究科），吉田　塁（東京大学大学院新領域創成科学研究科），小谷　潔（東京大学大学院新領域創成科学研究科），神保　泰彦（東京大学大学院新領域創成科学研究科）

著者名(英語): Takashi Numata (Graduate School of Frontier Sciences, The University of Tokyo), Yutaro Ogawa (Graduate School of Frontier Sciences, The University of Tokyo), Lui Yoshida (Graduate School of Frontier Sciences, The University of Tokyo), Kiyoshi Kotani (Graduate School of Frontier Sciences, The University of Tokyo), Yasuhiko Jimbo (Graduate School of Frontier Sciences, The University of Tokyo)

キーワード: 自律神経活動，呼吸性洞性不整脈，アイソメトリック運動　　autonomic nervous activity，respiratory sinus arrhythmia，isometric exercise

要約(英語): The autonomic nervous system is important in maintaining homeostasis by mediating the opposing effects of the sympathetic and parasympathetic nervous activity on organs. Although it is known that the amplitude of RSA (Respiratory Sinus Arrhythmia) is an index of parasympathetic nervous activity, it is difficult to estimate that activity in real-time in everyday situations. It is partly caused by body motions and extrasystoles. Also, automatic recognition of the R-wave on electrocardiograms is required for real-time analysis of RSA amplitude, there is an unresolved problem of false recognition of the R-wave. In this paper, we propose a method to evaluate the amplitude of RSA accurately using statistical processing with probabilistic models. Then, we estimate parasympathetic nervous activity during body motion and isometric exercise to examine the validity of the method. As a result, using the proposed method, we demonstrate that the amplitude of RSA can be extracted with false recognition of the R-wave. In addition, an appropriate threshold for the estimate is one or five percent because waveforms of RSA amplitude do not follow the abrupt changes of the parasympathetic nervous activity evoked by isometric exercise with the threshold at ten percent. Furthermore, the method using normal distribution is found to be more appropriate than that of chi-square distribution for statistical processing. Therefore, we expect that the proposed method can evaluate parasympathetic nervous activity with high accuracy in everyday situations.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.1 （2012） 特集：確率的最適化と機械学習の統計的設計と応用

本誌掲載ページ: 96-103 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/132/1/132_1_96/_article/-char/ja/