Annals of Biomedical Engineering

, Volume 39, Issue 2, pp 824–834 | Cite as

A Robust Method to Estimate Instantaneous Heart Rate from Noisy Electrocardiogram Waveforms

Article
First Online:
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Accepted:

Abstract

We propose a new algorithm for real-time estimation of instantaneous heart rate (HR) from noise-laden electrocardiogram (ECG) waveforms typical of unstructured, ambulatory field environments. The estimation of HR from ECG waveforms is an indirect measurement problem that requires differencing, which invariably amplifies high-frequency noise. We circumvented noise amplification by considering the estimation of HR as the solution of a weighted regularized least squares problem, which, in addition, directly provided analytically based confidence intervals (CIs) for the estimated HRs. To evaluate the performance of the proposed algorithm, we applied it to simulated data and to noise-laden ECG records that were collected during helicopter transport of trauma-injured patients to a trauma center. We compared the proposed algorithm with HR estimates produced by a widely used vital-sign travel monitor and a standard HR estimation technique, followed by postprocessing with Kalman filtering or spline smoothing. The simulation results indicated that our algorithm consistently produced more accurate HR estimates, with estimation errors as much as 67% smaller than those attained by the postprocessing methods, while the results with the field-collected data showed that the proposed algorithm produced much smoother and reliable HR estimates than those obtained by the vital-sign monitor. Moreover, the obtained CIs reflected the amount of noise in the ECG recording and could be used to statistically quantify uncertainties in the HR estimates. We conclude that the proposed method is robust to different types of noise and is particularly suitable for use in ambulatory environments where data quality is notoriously poor.

Keywords

Heart rate estimation Least squares Regularization Confidence intervals 
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Notes

Acknowledgments

We thank Y. Lu, S. Rajaraman, and C. Meng for providing stimulating feedback. This study was supported by a War Supplemental competitive grant managed by the Combat Casualty Care Directorate of the U.S. Army Medical Research and Materiel Command, Fort Detrick, MD.

Disclaimer

The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the U.S. Army or of the U.S. Department of Defense. This paper has been approved for public release with unlimited distribution.

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Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • Andrei V. Gribok
    • 1
    • 2
  • Xiaoxiao Chen
    • 1
  • Jaques Reifman
    • 1
  1. 1.Bioinformatics Cell, Telemedicine and Advanced Technology Research CenterU.S. Army Medical Research and Materiel CommandFort DetrickUSA
  2. 2.Nuclear Engineering Department of the University of TennesseeKnoxvilleUSA

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