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An Overview of Heart Rate Variability Metrics and Norms.

TLDR
Current perspectives on the mechanisms that generate 24 h, short-term (<5 min), and ultra-short-term HRV are reviewed, and the importance of HRV, and its implications for health and performance are reviewed.
Abstract
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.

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Journal ArticleDOI

Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring

TL;DR: In this paper, the correlation between EEG and heart rate variability (HRV) under stress conditions is analyzed and compared in rest, stress, and meditation conditions, and a one-way ANOVA and correlation coefficient are used for statistical analysis to explore the relationship between HRV features and features extracted from EEG.
Journal ArticleDOI

Heart rate variability in patients with anxiety disorders: A systematic review and meta‐analysis

TL;DR: Patients with anxiety disorders have been found to have lower heart rate variability than healthy individuals in some studies, but this was inconsistent and the influence of distinct diagnoses, study design, and demographic factors on the results was not comprehensively examined.
Journal ArticleDOI

Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus.

TL;DR: Compared with different severities of tetanus, raw data for both time and frequency measurements of HRV were reduced in those with ANSD compared with those without, indicating HRV may be a valuable tool in ANSD diagnosis.
Journal ArticleDOI

FLIRT: A feature generation toolkit for wearable data.

TL;DR: FLIRT as discussed by the authors is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction, and leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables.
Journal ArticleDOI

Use of a Biofeedback Breathing App to Augment Poststress Physiological Recovery: Randomized Pilot Study.

TL;DR: Results provide additional utility of biofeedback breathing in augmenting physiological recovery from psychological stress and suggest app-based relaxed breathing interventions could be effective in reducing cardiovascular disease risk.
References
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Journal ArticleDOI

Measuring agreement in method comparison studies

TL;DR: The 95% limits of agreement, estimated by mean difference 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie.
Journal ArticleDOI

Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control

TL;DR: It is shown that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz.
Journal ArticleDOI

Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog.

TL;DR: The spontaneous beat-to-beat oscillation in R-R interval during control recumbent position, 90° upright tilt, controlled respiration and acute and chronic β-adrenergic receptor blockade was analyzed, indicating that sympathetic nerves to the heart are instrumental in the genesis of low-frequency oscillations in R -R interval.
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Trending Questions (1)
What are healthy ranges for heartrate variability?

The paper provides an overview of HRV metrics and norms but does not specifically mention healthy ranges for heart rate variability.