Robust Estimation of Respiratory Rate Based on Linear Regression

  • J. Łęski
  • N. Henzel
  • A. Momot
  • M. Momot
  • J. Śledzik
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


One of the basic parameters which are of very importance in patients monitoring is respiratory rate (frequency of breathing). Previous studies show that there is possibility of estimating the respiratory rate from ECG signal using the modulation amplitude of this signal during the breath. This paper presents the method which is highly sensitive to the ECG amplitude changes and also robust to disturbances, usually accompanying the signal. The idea which distinguishes the method from other is to use changes of ECG amplitude in consecutive heart cycles on the basis of the entire QRS complex. The changes are determined by robust regression analysis (which leads to iterative algorithm) and the regression coefficients are transformed to the frequency domain.


Differential Quadrature Impulsive Noise Unbiased Minimum Variance Unbiased Minimum Variance Estimation International Electrotechnical Commission Standard 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • J. Łęski
    • 1
    • 3
  • N. Henzel
    • 1
    • 3
  • A. Momot
    • 2
  • M. Momot
    • 3
  • J. Śledzik
    • 3
  1. 1.Institute of ElectronicsSilesian University of TechnologyPoland
  2. 2.Institute of Computer ScienceSilesian University of TechnologyPoland
  3. 3.Institute of Medical Technology and EquipmentPoland

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