High‐Accuracy Extraction of Respiratory Sinus Arrhythmia with Statistical Processing Using Normal Distribution. Issue 9 (6th August 2013)

• Record Type: Journal Article
• Authors: Numata, Takashi; Ogawa, Yutaro; Yoshida, Lui; Kotani, Kiyoshi; Jimbo, Yasuhiko
• Is Part Of: Electronics and communications in Japan. Volume 96:Issue 9(2013:Sep.)
• Page Start: 23
• Page End: 32
• Publication Date: 2013-08-06
• Subjects: Electronics -- Periodicals; Telecommunication -- Periodicals; 621.6805
• DOI: 10.1002/ecj.11441
• Languages: English
• ISSNs: 1942-9533

Abstract

<abstract abstract-type="main" id="ecj11441-abs-0001"> <title>SUMMARY</title> <p id="ecj11441-para-0006">The autonomic nervous system is important in maintaining homeostasis by mediating the opposing effects of 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. This is in part because of body motion and extrasystole. Further, automatic recognition of the R wave on an electrocardiogram is required for a real‐time analysis of the RSA amplitude, and there is an unresolved problem of false recognition of the R wave. In this paper we propose a method of evaluating the amplitude of RSA accurately by statistical processing with probabilistic models. We then 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 in the presence of false recognition of the R wave. An appropriate threshold for the estimate is 1 or 5% because the waveforms of the RSA amplitude do not follow the abrupt changes of parasympathetic nervous activity induced by isometric exercise with the threshold at 10%. Furthermore, the method using the normal distribution is found to be more appropriate than that of the chi‐square distribution for statistical processing. Therefore, we expect that the proposed method can be used to evaluate parasympathetic nervous activity with high accuracy in everyday situations. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(9): 23–32, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11441</p> </abstract>