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Essential R Peak Detector Based on the Polynomial Fitting

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 77))

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Abstract

R peaks detection is one of the uncomplicated tasks, successfully solved for the stationary ECG systems. The finite computational capacities of the recent mobile devices circumscribe applying powerful methods for ECG waves recognition. Proposed in this paper method of R peaks detection is based on the evaluation of the shape of an m sample segment and oriented to develop specific mobile applications conjugated with a portable ECG device. R peaks recognition is performed with the parameters, obtained as the result of polynomial fitting an m sample segment and characterizing the segment shape and position of the segment central point relatively the focus point. The complex of criteria for R peak distinguish was defined as the result of analysis of testing signals from the MIT-BIH Arrhythmia Database. The algorithm for R peaks searching in an optionally defined time interval was developed and can be used for the formation of RR intervals array for the heart rate monitoring. The sensitivity of the method for the raw signals with positive R peaks and is 99.6%.

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Authors and Affiliations

  1. Yaroslav Mudryi National Law University, Kharkiv, Ukraine

    Olga Velychko

  2. National University of Radio Electronics, Kharkiv, Ukraine

    Oleh Datsok & Iryna Perova

Authors
  1. Olga Velychko
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  2. Oleh Datsok
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  3. Iryna Perova
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Editor information

Editors and Affiliations

  1. Department of Physics, Kherson State University, Kherson, Ukraine

    Sergii Babichev

  2. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Velychko, O., Datsok, O., Perova, I. (2022). Essential R Peak Detector Based on the Polynomial Fitting. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_10

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