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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

Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation.

TL;DR: In this paper, a group of university students to whom olfactory training lasting 3 months was administered, and the analysis of electrocardiogram (ECG) and galvanic skin response (GSR) signals at the beginning and at the end of the training period, they observed different autonomic responses, with higher parasympathetically-mediated response with respect to the first evaluation, suggesting that an increased familiarity to the proposed stimuli would lead to a higher tendency towards relaxation.
Journal ArticleDOI

Effects of high-intensity intervals and moderate-intensity exercise on baroreceptor sensitivity and heart rate variability during recovery.

TL;DR: Previously a 12-hour minimum restriction from exercise was required before participation in HRV/BRS studies • Recovery from moderate-intensity exercise for HRV and BRS metrics was <60 minutes; whereas, high-intensity intervals led to alterations for approximately 60 minutes • Spontaneous HRVs demonstrated high levels of within-day reproducibility.
Journal ArticleDOI

Autonomic control is a source of dynamical chaos in the cardiovascular system

TL;DR: The largest Lyapunov exponent and the correlation dimension for the 4-h experimental interbeat intervals and the chaotic signals generated by the mathematical model of the cardiovascular system are estimated.
Journal ArticleDOI

Implementing Mobile HRV Biofeedback as Adjunctive Therapy During Inpatient Psychiatric Rehabilitation Facilitates Recovery of Depressive Symptoms and Enhances Autonomic Functioning Short-Term: A 1-Year Pre-Post-intervention Follow-Up Pilot Study.

TL;DR: HRVBF as adjuvant therapy during inpatient psychiatric rehabilitation facilitated depression recovery and emphasized HRVBF’s value as complementary therapy regardless of concurrent treatments.
Proceedings ArticleDOI

Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes

TL;DR: A machine learning model to detect hypoglycemia on basis of data from smartwatch sensors gathered in a proof-of-concept study and uses SHAP (SHapley Additive exPlanations) values for feature attribution and a method for subsequently explaining the model decision in a comprehensible way on smartwatches.
References
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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.