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

We present a simple efficient algorithm for the derivation of a heart rate signal from the electrocardiogram. We demonstrate that the amplitude spectrum of this heart rate signal more closely matches that of the input signal to an integral pulse frequency modulation (IPFM) model of the heart's pacemaker than do the spectra of other ECG-derived heart rate signals. The applicability of this algorithm in cross-spectral analysis between heart rate and other physiologic signals is also discussed.

An Efficient Algorithm for Spectral Analysis of Heart Rate Variability

Different methods for spectral analysis of the heart rate signal?considered as series of point events?are used in studies on heart rate variability. This paper compares these methods, focusing on the two principal ones: the interval spectrum, i.e., the spectrum of the interval series, and the spectrum of counts, which is related to the representation of the event series as a series of spikes (delta functions). Both autospectra are estimated for experimental heart rate data and are shown to produce similar results. This similarity is proven analytically, and it is shown that for small variations in interval length, the ratio of these spectra is PI(f)/PC(f) = [sin(?f?)/(?f?)]2, with PI and PC the interval spectrum and the spectrum of counts, respectively, f the frequency, and ?the mean interval length. It is concluded that both autospectra are equivalent for the considered heart rate data, but that, when relating the heart rate signal to other signals (e.g., respiration, blood pressure) by means of cross spectra, the technique to be used depends on the characteristics of the second signal.

Comparing Spectra of a Series of Point Events Particularly for Heart Rate Variability Data

Research into cardiovascular variabilities intersects both human physiology and quantitative modelling. This is because respiratory and Mayer wave (or 10 s) cardiovascular oscillations represent the integrated control of a system through both autonomic branches by systemic haemodynamic changes within a fluid-filled, physical system. However, our current precise measurement of short-term cardiovascular fluctuations does not necessarily mean we have an adequate understanding of them. Empirical observation suggests that both respiratory and Mayer wave fluctuations derive from mutable autonomic and haemodynamic inputs. Evidence strongly suggests that respiratory sinus arrhythmia both contributes to and buffers respiratory arterial pressure fluctuations. Moreover, even though virtual abolition of all R-R interval variability by cholinergic blockade suggests that parasympathetic stimulation is essential for expression of these variabilities, respiratory sinus arrhythmia does not always reflect a purely vagal phenomenon. The arterial baroreflex has been cited as the mechanism for both respiratory and Mayer wave frequency fluctuations. However, data suggest that both cardiac vagal and vascular sympathetic fluctuations at these frequencies are independent of baroreflex mechanisms and, in fact, contribute to pressure fluctuations. Results from cardiovascular modelling can suggest possible sources for these rhythms. For example, modelling originally suggested low frequency cardiovascular rhythms derived from intrinsic delays in baroreceptor control, and experimental evidence subsequently corroborated this possibility. However, the complex stochastic relations between and variabilities in these rhythms indicate no single mechanism is responsible. If future study of cardiovascular variabilities is to move beyond qualitative suggestions of determinants to quantitative elucidation of critical physical mechanisms, both experimental design and model construction will have to be more trenchant.

Short‐term cardiovascular oscillations in man: measuring and modelling the physiologies

The time-domain signals representing the heart rate variability (HRV) in the presence of an ectopic beat exhibit a sharp transient at the position of the ectopic beat, which corrupts the signal, particularly the power spectral density (PSD) of the HRV. Consequently, there is a need for correction of this type of beat prior to any HRV analysis. This paper deals with the PSD estimation of the HRV by means of the heart timing (HT) signal when ectopic beats are present. These beat occurrence times are modeled from a generalized, continuous time integral pulse frequency modulation model and, from this point of view, a specific method for minimizing the effect of the presence of ectopic beats is presented to work together with the HT signal. By using both, a white noise driven autoregressive model of the HRV signal with artificially introduced ectopic beats and actual heart rate series including ectopic beats, the more usual methods of HRV spectral estimation are compared. Results of the PSD estimation error function of the number of ectopic beats are presented. These results demonstrate that the proposed method has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.

/pdf/analysis-of-heart-rate-variability-in-the-presence-of-ewzey31wa2.pdf

Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal

The analysis of fluctuations in heart rate or heart rate variability (HRV) has found applications in, among others, the study of the neural cardiovascular system and ergonomic psychology. In particular, the study of the frequency components of HRV is becoming increasingly important. A method for the computation of HRV spectra directly from the heart beat event series as derived from the electrocardiogram has been developed. Because of the computational efficiency achieved, this method is implemented on a personal computer. Apart from an external QRS detector, a completely stand-alone system is realized. User interaction takes place on a menu card display basis. The system can operate at both real time and up to eightfold increased speed. The resultant spectra are displayed as histograms. Different ways of smoothing and segment averaging are possible.

Cees J. Van Spronsen

Papers

The Measurement of Heart Rate Variability Spectra with the Help of a Personal Computer