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Heart Rate Variability Analysis: How Much Artifact Can We Remove?

David C Sheridan1,2, Ryan Dehart1, Amber Lin1,2

  • 1Department of Emergency Medicine, Oregon Health & Science University, Portland, USA.

Psychiatry Investigation
|October 5, 2020
PubMed
Summary
This summary is machine-generated.

Small errors in beat-to-beat interval (BBI) data from wearable devices minimally impact time-domain heart rate variability (HRV) metrics. Frequency-domain HRV is more sensitive to BBI inaccuracies, crucial for mental health wearable technology development.

Keywords:
ArtifactHeart rate variabilityMental healthSuicidality

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Physiology
  • Wearable Technology

Background:

  • Heart rate variability (HRV) reflects autonomic nervous system function.
  • Wearable devices for health monitoring are susceptible to motion artifacts.
  • Artifacts can compromise the accuracy of HRV analysis.

Purpose of the Study:

  • To assess the impact of minor errors in beat-to-beat interval (BBI) selection on HRV.
  • To establish a baseline for artifact tolerance in HRV analysis.
  • To inform the development of mental health wearable technology.

Main Methods:

  • Sub-analysis of a prospective observational clinical trial (NCT03030924).
  • Simulated common artifacts by manipulating HRV tracings from 10 subjects.
  • Used data from a wrist-worn wearable monitor without pre-existing artifact.

Main Results:

  • Time-domain metrics (RMSSD, SDNN) remained clinically stable with up to 5 incorrect BBIs or 36% BBI removal.
  • High-frequency HRV demonstrated significant changes with >2 incorrect BBIs or any BBI removal.
  • Artifact tolerance varied between time and frequency domain HRV measures.

Conclusions:

  • Time-domain HRV metrics exhibit greater resilience to artifact than frequency-domain metrics.
  • Researchers using wearable tech for mental health should consider these findings.
  • Improved data quality in HRV studies is achievable by understanding artifact impact.