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Benchmarking heart rate variability toolboxes
Adriana N Vest1, Qiao Li2, Chengyu Liu2
1Department of Biomedical Informatics, Emory University School of Medicine, Woodruff Memorial Research Bldg, Suite 4100, 101 Woodruff Circle, Atlanta, GA, United States; Department of Epidemiology, Rollins School of Public Health at Emory University, 1518 Clifton Road NE, Room 3053, Atlanta, GA, United States.
Insights
A new Matlab program for heart rate variability (HRV) analysis yields results similar to existing tools. This open-source software offers validated preprocessing and arrhythmia detection for reliable autonomic function assessment.
Area of Science:
- Physiology
- Biomedical Engineering
- Computational Biology
Background:
- Heart rate variability (HRV) metrics are valuable for assessing autonomic function, cardiovascular health, and wellness.
- Lack of standardized methods for HRV preprocessing and analysis hinders consistent research and clinical application.
Purpose of the Study:
- To introduce a comprehensive, open-source, modular Matlab program for calculating HRV.
- To ensure evidence-based algorithms and standardized output formats for HRV analysis.
- To compare the performance of the new HRV software with an established C-based toolbox.
Main Methods:
- Development of a modular HRV analysis program in Matlab.
- Implementation of evidence-based algorithms for HRV calculation.
- Comparative analysis against a widely used HRV toolbox (PhysioNet.org).
Main Results:
- Substantially similar HRV results were obtained using the new software with high-quality electrocardiograms (ECGs) free from arrhythmias.
- The developed software demonstrated equivalent performance compared to an established HRV analysis tool.
Conclusions:
- The new Matlab HRV software provides equivalent performance to existing tools.
- Includes validated modules for preprocessing, signal quality assessment, and arrhythmia detection.
- Aims to enhance standardization, repeatability, and reduce errors in HRV analysis, particularly with noisy or arrhythmic ECG data.
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.
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