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Annals of Biomedical Engineering

, Volume 42, Issue 10, pp 2072–2083 | Cite as

Electrocardiogram Derived Respiratory Rate from QRS Slopes and R-Wave Angle

  • Jesús LázaroEmail author
  • Alejandro Alcaine
  • Daniel Romero
  • Eduardo Gil
  • Pablo Laguna
  • Esther Pueyo
  • Raquel Bailón
Article

Abstract

A method for estimating respiratory rate from electrocardiogram (ECG) signals is presented. It is based on QRS slopes and R-wave angle, which reflect respiration-induced beat morphology variations. The 12 standard leads, 3 leads from vectorcardiogram (VCG), and 2 additional non-standard leads derived from VCG loops were analyzed. The following series were studied as ECG derived respiration (EDR) signals: slope between the peak of Q and R waves, slope between the peak of R and S waves, and the R-wave angle. Information from several EDR signals was combined in order to increase the robustness of estimation. Evaluation is performed over two databases containing ECG and respiratory signals simultaneously recorded during two clinical tests with different characteristics: tilt test, representing abrupt cardiovascular changes, and stress test representing a highly non-stationary and noisy environment. A combination of QRS slopes and R-wave angle series derived from VCG leads obtained a respiratory rate estimation relative error of 0.50 ± 4.11% (measuring 99.84% of the time) for tilt test and 0.52 ± 8.99% (measuring 96.09% of the time) for stress test. These results outperform those obtained by other reported methods, both in tilt and stress testing.

Keywords

ECG-derived respiration (EDR) Exercise Tilt test Respiratory frequency Robustness QRS slopes R-wave angle Stress testing 

Notes

Acknowledgments

Thanks to Dr. A. Mincholé, Dr. L. Sörnmo, Dr. G. Blain and Dr. S. Bermon for providing the authors with datasets. This work is supported by Universidad de Zaragoza under fellowship PIFUZ-2011-TEC-A-003, by Ministerio de Economía y Competitividad (MINECO), FEDER; under projects TEC2010-21703-C03-02, TEC2010-19410, TEC2013-42140-R, TIN2013-41998-R and FIS-PI12/00514, by CIBER de Bioingeniería, Biomateriales y Nanomedicina through Instituto de Salud Carlos III, and by Grupo Consolidado BSICoS from DGA (Aragón) and European Social Fund (EU). Esther Pueyo acknowledges the financial support of Ramón y Cajal program from MINECO.

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Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Jesús Lázaro
    • 1
    • 2
    Email author
  • Alejandro Alcaine
    • 1
    • 2
  • Daniel Romero
    • 2
    • 3
  • Eduardo Gil
    • 1
    • 2
  • Pablo Laguna
    • 1
    • 2
  • Esther Pueyo
    • 1
    • 2
  • Raquel Bailón
    • 1
    • 2
  1. 1.BSICoS group, Aragón Institute of Engineering Research (I3A), IIS AragónUniversity of ZaragozaZaragozaSpain
  2. 2.Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)MadridSpain
  3. 3.Laboratoire Traitement du Signal et de l’ImageUniversité de Rennes IRennesFrance

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