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Author Notes:

Corresponding author: A.N. Vest (adriana.vest@emory.edu).

The authors also wish to thank George Moody and Joe Mietus for posting the C HRV toolbox which serves as the baseline comparison point for this article.


Research Funding:

The authors wish to acknowledge the National Institutes of Health (Grant # NIH K23 HL127251) and Emory University for their financial support of this research.

GC is partially supported by National Science Foundation Award 1636933 and the National Institutes of Health, the Fogarty International Center and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant number 1R21HD084114-01.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the National Institutes of Health.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Cardiac & Cardiovascular Systems
  • Cardiovascular System & Cardiology
  • Heart rate variability
  • Toolbox benchmarking
  • Peak detection
  • Physiological signal processing
  • BEAT

Benchmarking heart rate variability toolboxes


Journal Title:

Journal of Electrocardiology


Volume 50, Number 6


, Pages 744-747

Type of Work:

Article | Post-print: After Peer Review


Background Heart rate variability (HRV) metrics hold promise as potential indicators for autonomic function, prediction of adverse cardiovascular outcomes, psychophysiological status, and general wellness. Although the investigation of HRV has been prevalent for several decades, the methods used for preprocessing, windowing, and choosing appropriate parameters lack consensus among academic and clinical investigators. Methods A comprehensive and open-source modular program is presented for calculating HRV implemented in Matlab with evidence-based algorithms and output formats. We compare our software with another widely used HRV toolbox written in C and available through PhysioNet.org. Results Our findings show substantially similar results when using high quality electrocardiograms (ECG) free from arrhythmias. Conclusions Our software shows equivalent performance alongside an established predecessor and includes validated tools for performing preprocessing, signal quality, and arrhythmia detection to help provide standardization and repeatability in the field, leading to fewer errors in the presence of noise or arrhythmias.

Copyright information:

© 2017 Elsevier Inc.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Creative Commons License

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