Detection of Cardiac Diseases from ECG Using Nonlinear Stochastic Filters

• Matoba Chisato

  Department of Architecture and Mechatronics, Oita University

• Imamasu Keisuke

  Department of Architecture and Mechatronics, Oita University

• Suemitsu Haruo

  Department of Architecture and Mechatronics, Oita University

• Matsuo Takami

  Department of Architecture and Mechatronics, Oita University

抄録

The dynamics of heart rhythms have been widely studied because of the key aspects in the physiology of living beings. Mathematical modeling of heart rhythms is the objective of many research efforts. Since the component elements exhibit oscillatory behavior, they can be modeled as nonlinear oscillators. Gois et al.[1] have recently proposed a mathematical model to describe heart rhythms considering three-coupled Van der Pol oscillators. In this paper, we constitute the particle filter and the ensemble Kalman filter from this model to detect the cardiac diseases by estimating the coupling parameters. In particular, we use two types of ECG data calculated by the discrete Van der Pol oscillators, which are the initial-time responses including the transient state and excluding the transient state. Comparing the particle filter with the ensemble Kalman filter, we indicate that the latter has better estimation performance.

収録刊行物

• Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications

  Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2015 (0), 59-64, 2015

  システム制御情報学会ストカスティックシステムシンポジウム

キーワード

• Materials Science (miscellaneous)

詳細情報

• CRID: 1390001205763311360
• NII論文ID: 130005107468
• DOI: 10.5687/sss.2015.59
• ISSN: 21884749
• ISSN: 21884730
• Web Site: https://www.jstage.jst.go.jp/article/sss/2015/0/2015_59/_pdf
• 本文言語コード: en
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