An Overview of Heart Rate Variability Metrics and Norms
By F. Shaffer, J. Ginsberg. Frontiers in Public Health, 2017
6,176 citations recorded by the source corpus.
Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine
View Paper: https://www.semanticscholar.org/paper/303d5c4bd2de7d02f53f43b33ec92f50c91e67bd
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In Biomedical Engineering
- Brinnae Bent
- Fedaa Abd-Alhamid
- Benjamin A. Goldstein
- Warren A. Kibbe
- Sanchita Paul
- John Kaiser Calautit
- Jessilyn Dunn
- Shruti Gedam
In Cardiology and Cardiovascular Medicine
- Brinnae Bent
- Fedaa Abd-Alhamid
- Benjamin A. Goldstein
- Warren A. Kibbe
- Sanchita Paul
- John Kaiser Calautit
- Jessilyn Dunn
- Shruti Gedam
Citations in the corpus
- A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021, background): “The measurement time of observed HRV may range from > 1 minute to 24 hours during the monitoring period [73].”
- Evaluating the impact of viewing location on view perception using a virtual environment (2020, methodology): “The ratio of LF to HF power (LF/HF) has been used as an indicator for stress level [53], whereby a higher ratio indicates elevated stress level and a low value indicates more relaxation [51,54, 55].”
- Investigating sources of inaccuracy in wearable optical heart rate sensors (2020, methodology): “In addition to HR, we examined HR variability (HRV), a clinically relevant diagnostic metric that can be derived from PPG signals and is a widely used metric of autonomic nervous system function.(43) The standard HRV metrics we examined, included the time-domain metrics, included mean HRV, minimum H”
- Multimodal assessment of acute stress dynamics using an aversive video paradigm (AVP) (2024, unknown): “HRV was determined using the Root Mean Square of Successive Differences (RMSSD) method (Shaffer & Ginsberg, 2017) via a custom-made MATLAB script for identical 1-minute bins as for HR.”