Components of heart rate variability have attracted considerable attention in psychology and medicine and have become important dependent measures in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability, however, remain complex issues and are fraught with pitfalls. The present report (a) examines the physiological origins and mechanisms of heart rate variability, (b) considers quantitative approaches to measurement, and (c) highlights important caveats in the interpretation of heart rate variability. Summary guidelines for research in this area are outlined, and suggestions and prospects for future developments are considered.

https://www.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1469-8986.1997.tb02140.x

Heart rate variability: Origins, methods, and interpretive caveats

Frequency-domain analyses of R-R intervals are used widely to estimate levels of autonomic neural traffic to the human heart. Because respiration modulates autonomic activity, we determined for nine healthy subjects the influence of breathing frequency and tidal volume on R-R interval power spectra (fast-Fourier transform method). We also surveyed published literature to determine current practices in this burgeoning field of scientific inquiry. Supine subjects breathed at rates of 6, 7.5, 10, 15, 17.1, 20, and 24 breaths/min and with nominal tidal volumes of 1,000 and 1,500 ml. R-R interval power at respiratory and low (0.06-0.14 Hz) frequencies declined significantly as breathing frequency increased. R-R interval power at respiratory frequencies was significantly greater at a tidal volume of 1,500 than 1,000 ml. Neither breathing frequency nor tidal volume influenced average R-R intervals significantly. Our review of studies reporting human R-R interval power spectra showed that 51% of the studies controlled respiratory rate, 11% controlled tidal volume, and 11% controlled both respiratory rate and tidal volume. The major implications of our analyses are that breathing parameters strongly influence low-frequency as well as respiratory frequency R-R interval power spectra and that this influence is largely ignored in published research.

Important influence of respiration on human R-R interval power spectra is largely ignored

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal's second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

/pdf/a-review-on-wearable-photoplethysmography-sensors-and-their-3kmqsjhauj.pdf

A review on wearable photoplethysmography sensors and their potential future applications in health care.

1. Time domain summary statistics and frequency domain parameters can be used to measure heart rate variability. More recently, qualitative methods including the Poincare plot have been used to evaluate heart rate variability. The aim of this study was to validate a novel method of quantitative analysis of the Poincare plot using conventional statistical techniques. 2. Beat-to-beat heart rate variability was measured over a relatively short period of time (10-20 min) in 12 healthy subjects aged between 20 and 40 years (mean 30 +/- 7 years) during (i) supine rest, (ii) head-up tilt (sympathetic activation, parasympathetic nervous system activity withdrawal), (iii) intravenous infusion of atropine (parasympathetic nervous system activity withdrawal), and (iv) after overnight administration of low-dose transdermal scopolamine (parasympathetic nervous system augmentation). 3. The "width' of the Poincare plot, as quantified by SD delta R-R (the difference between successive R-R intervals), was determined at rest (median 48.9, quartile range 20 ms) and found to be significantly reduced during tilt (median 19.1, quartile range 13.7 ms, P < 0.01) and atropine administration (median 7.1, quartile range 5.7 ms, P < 0.01) and increased by scopolamine (median 79.3, quartile range 33 ms, P < 0.01). Furthermore, log variance of delta R-R intervals correlated almost perfectly with log high-frequency (0.15-0.4 Hz) power (r = 0.99, P < 0.01). 4. These findings strongly suggest that the "width' of the Poincare plot is a measure of parasympathetic nervous system activity. The Poincare plot is therefore a quantitative visual tool which can be applied to the analysis of R-R interval data gathered over relatively short time periods.

Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans

Measurement and analysis methods of heart rate and respiration for use in applied environments

A definition of heart-rate variability (h.r.v.) is given. The use of h.r.v. measurement in both clinical applications and the neural cardiovascular research is discussed. For the latter applications, four different signals describing h.r.v. are reviewed. It is shown that these signals are based on modifications of one model, namely the integral pulse frequency modulator. In Part 2, a hardware device for measuring h.r.v. based on one of these modifications is described.

A. J. R. M. Coenen

Papers

Measurement of heart-rate variability: Part 1-Comparative study of heart-rate variability analysis methods.