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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An Overview of Heart Rate Variability Metrics and Norms.

Kubios HRV - Heart rate variability analysis software

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

/pdf/advancements-in-noncontact-multiparameter-physiological-3t98r5y6lj.pdf

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Psychophysiological research integrating heart rate variability (HRV) has increased during the last two decades, particularly given the fact that HRV is able to index cardiac vagal tone. Vagal tone, which represents the activity of the parasympathetic system, is acknowledged to be linked with many phenomena relevant for psychophysiological research, including self-regulation at the cognitive, emotional, social, and health levels. The ease of HRV collection and measurement coupled with the fact it is relatively affordable, non-invasive and pain free makes it widely accessible to many researchers. This ease of access should not obscure the difficulty of interpretation of HRV findings that can be easily misconstrued, however this can be controlled to some extent through correct methodological processes. Standards of measurement were developed two decades ago by a Task Force within HRV research, and recent reviews updated several aspects of the Task Force paper. However, many methodological aspects related to HRV in psychophysiological research have to be considered if one aims to be able to draw sound conclusions, which makes it difficult to interpret findings and to compare results across laboratories. Those methodological issues have mainly been discussed in separate outlets, making difficult to get a grasp on them, and thus this paper aims to address this issue. It will help to provide psychophysiological researchers with recommendations and practical advice concerning experimental designs, data analysis, and data reporting. This will ensure that researchers starting a project with HRV and cardiac vagal tone are well informed regarding methodological considerations in order for their findings to contribute to knowledge advancement in their field.

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Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting.

BACKGROUND\nObesity in childhood is associated with increased cardiovascular risk. It is uncertain whether this risk is attenuated in persons who are overweight or obese as children but not obese as adults.\n\n\nMETHODS\nWe analyzed data from four prospective cohort studies that measured childhood and adult body-mass index (BMI, the weight in kilograms divided by the square of the height in meters). The mean length of follow-up was 23 years. To define high adiposity status, international age-specific and sex-specific BMI cutoff points for overweight and obesity were used for children, and a BMI cutoff point of 30 was used for adults.\n\n\nRESULTS\nData were available for 6328 subjects. Subjects with consistently high adiposity status from childhood to adulthood, as compared with persons who had a normal BMI as children and were nonobese as adults, had an increased risk of type 2 diabetes (relative risk, 5.4; 95% confidence interval [CI], 3.4 to 8.5), hypertension (relative risk, 2.7; 95% CI, 2.2 to 3.3), elevated low-density lipoprotein cholesterol levels (relative risk, 1.8; 95% CI, 1.4 to 2.3), reduced high-density lipoprotein cholesterol levels (relative risk, 2.1; 95% CI, 1.8 to 2.5), elevated triglyceride levels (relative risk, 3.0; 95% CI, 2.4 to 3.8), and carotid-artery atherosclerosis (increased intima-media thickness of the carotid artery) (relative risk, 1.7; 95% CI, 1.4 to 2.2) (P ≤ 0.002 for all comparisons). Persons who were overweight or obese during childhood but were nonobese as adults had risks of the outcomes that were similar to those of persons who had a normal BMI consistently from childhood to adulthood (P>0.20 for all comparisons).\n\n\nCONCLUSIONS\nOverweight or obese children who were obese as adults had increased risks of type 2 diabetes, hypertension, dyslipidemia, and carotid-artery atherosclerosis. The risks of these outcomes among overweight or obese children who became nonobese by adulthood were similar to those among persons who were never obese. (Funded by the Academy of Finland and others.).

Childhood Adiposity, Adult Adiposity, and Cardiovascular Risk Factors EDITORIAL COMMENT

An advanced, simple to use, detrending method to be used before heart rate variability analysis (HRV) is presented. The method is based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. The effect of the detrending on time- and frequency-domain analysis of HRV is studied.

An advanced detrending method with application to HRV analysis

Software for advanced HRV analysis

Where to put your best foot forward: Psycho-physiological responses to walking in natural and urban environments

A software for advanced heart rate variability (HRV) analysis is presented. The software includes adaptable tools for correcting artifacts and for removing low frequency trend components. The analysis options of the software include all the commonly used time-domain, frequency-domain and nonlinear HRV parameters. Analysis results can be saved as a PDF report, ASCII text file or Matlab MAT file. The software is easy to use through its compact graphical user interface. Together with a high-quality heart rate monitor, capable of recording beat-to-beat RR intervals, this freely distributed software forms a complete system for HRV analysis.

Mika P. Tarvainen

Papers

Kubios HRV - Heart rate variability analysis software

An advanced detrending method with application to HRV analysis

Software for advanced HRV analysis

Where to put your best foot forward: Psycho-physiological responses to walking in natural and urban environments

Kubios HRV — A Software for Advanced Heart Rate Variability Analysis