Since the pioneering studies of the 1960s, heart rate variability (HRV) has become an increasingly used non-invasive tool for examining cardiac autonomic functions and dysfunctions in various populations and conditions. Many calculation methods have been developed to address these issues, each with their strengths and weaknesses. Although its interpretation may remain difficult, this technique provides, from a non-invasive approach, reliable physiological information that was previously inaccessible, in many fields including death and health prediction, training and overtraining, cardiac and respiratory rehabilitation, sleep-disordered breathing, large cohort follow-ups, children’s autonomic status, anesthesia, or neurophysiological studies. In this context, we developed HRVanalysis, a software to analyse HRV, used and improved for over 20 years and, thus, designed to meet laboratory requirements. The main strength of HRVanalysis is its wide application scope. In addition to standard analysis over short and long periods of RR intervals, the software allows time-frequency analysis using wavelet transform as well as analysis of autonomic nervous system status on surrounding scored events and on preselected labeled areas. Moreover, the interface is designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in TXT files directly employable in statistical softwares. Recordings can arise from RR or EKG files of different types such as cardiofrequencemeters, holters EKG, polygraphs, and data acquisition systems. HRVanalysis can be downloaded freely from the Web page at: anslabtools.univ-st-etienne.fr. HRVanalysis is meticulously maintained and developed for in-house laboratory use. In this article, after a brief description of the context, we present an overall view of HRV analysis and we describe the methodological approach of the different techniques provided by the software.

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HRVanalysis: A Free Software for Analyzing Cardiac Autonomic Activity.

Non-invasive autonomic evaluation has used fast Fourier transform (FFT) to assign a range of low (LF) and high frequencies (HF) as markers of sympathetic and parasympathetic influences, respectively However, FFT cannot be applied to brief transient phenomena, such as those observed on performing autonomic tests where the acute changes of cardiovascular signals (blood pressure and heart rate) that represent the first and most important stage of the autonomic performance towards a new state of equilibrium occur Wavelet analysis has been proposed as a method to overcome and complement information taken exclusively in the frequency domain With discrete wavelet transform (DWT), a time-frequency analysis can be done, allowing the visualization in time of the contribution of LF and HF to the observed changes of a particular signal In this study, we evaluate with wavelets the acute changes in R-R intervals and systolic blood pressure that are observed in normal subjects during four classical autonomic tests: head-up tilt (HUT), cold pressor test (CPT), deep breathing (DB) and Valsalva manoeuvre (VM) Continuous monitoring of ECG and blood presure was performed Also LF, HF and LF/HF were calculated Consistent with previous interpretations, data showed an increase of sympathetic activity in HUT, CPT and VM On DB, results reflected an increase in parasympathetic activity and frequencies In conclusion, when compared with FFT, wavelet analysis allows the evaluation of autonomic variability during short and non-stationary periods of time and may constitute a useful advance in the assessment of autonomic function in both physiological and pathological conditions

https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/expphysiol.2007.038026

Wavelet analysis of autonomic outflow of normal subjects on head‐up tilt, cold pressor test, Valsalva manoeuvre and deep breathing

Swine models in the design of more effective medical countermeasures against organophosphorus poisoning.

In this study, a framework for the characterization of the dynamic interactions between RR variability (RRV) and systolic arterial pressure variability (SAPV) is proposed. The methodology accounts for the intrinsic non-stationarity of the cardiovascular system and includes the assessment of both the strength and the prevalent direction of local coupling. The smoothed pseudo-Wigner–Ville distribution (SPWVD) is used to estimate the time–frequency (TF) power, coherence, and phase-difference spectra with fine TF resolution. The interactions between the signals are quantified by time-varying indices, including the local coupling, phase differences, time delay, and baroreflex sensitivity (BRS). Every index is extracted from a specific TF region, localized by combining information from the different spectra. In 14 healthy subjects, a head-up tilt provoked an abrupt decrease in the cardiovascular coupling; a rapid change in the phase difference (from 0.37 ± 0.23 to −0.27 ± 0.22 rad) and time delay (from 0.26 ± 0.14 to −0.16 ± 0.16 s) in the high-frequency band; and a decrease in the BRS (from 23.72 ± 7.66 to 6.92 ± 2.51 ms mmHg−1). In the low-frequency range, during a head-up tilt, restoration of the baseline level of cardiovascular coupling took about 2 min and SAPV preceded RRV by about 0.85 s during the whole test. The analysis of the Eurobavar data set, which includes subjects with intact as well as impaired baroreflex, showed that the presented methodology represents an improved TF generalization of traditional time-invariant methodologies and can reveal dysfunctions in subjects with baroreflex impairment. Additionally, the results also suggest the use of non-stationary signal-processing techniques to analyze signals recorded under conditions that are usually supposed to be stationary.

https://iopscience.iop.org/article/10.1088/0967-3334/33/3/315/pdf

Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time–frequency analysis

Prosthetic heart valves (PHVs) are engineered devices used for replacing diseased natural cardiac valves. This article presents several investigational techniques for the evaluation of the performance of these clinical devices, whose implantation is not completely free of drawbacks. The state-of-the-art in the technological approach for PHV testing is addressed. As the fluid dynamics of PHVs are particularly complex, the main focus will be on experimental velocimetric techniques and computational analysis. A methodology for the analysis of the valve’s signature, in terms of its characteristic sound in the opening and closing phases, is also presented. The aforementioned techniques are necessary to guarantee an operational life of the implanted device as free as possible from clinical complications. It can be realistically expected that this characterization will help designers in improving PHV performance.

Innovative technologies for the assessment of cardiovascular medical devices: state-of-the-art techniques for artificial heart valve testing

In this paper, the discrete wavelet transform (DWT) was applied to analyze the fluctuations in RR interval and systolic arterial pressure (SAP) recorded from eight a-chloralose anesthetized pigs. Our aim was to characterize the autonomic modulation before and after cardiac autonomic blockade and during baroreflex function tests. The instantaneous power of decomposed low-frequency (LF) and high-frequency (HF) components was used for a time-variant spectral analysis. Our results suggested that transient events and changes in autonomic modulation were detected with high temporal resolution. A nonlinear relationship between RR interval and SAP during pharmacologically induced changes in blood pressure was found, when the superimposed effect of respiratory sinus arrhythmia was removed. In addition, the baroslopes were nearly linear when both the LF and HF components were removed using DWT decomposition.

Wavelet decomposition of cardiovascular signals for baroreceptor function tests in pigs

In this paper the function of baroreceptive mechanisms in anesthetized pigs were assessed during pharmacological baroreceptor function tests. Using the discrete wavelet transform the respiratory related high-frequency component was decomposed from the beat-to-beat heart period and systolic arterial pressure variability signals. The pigs were mechanically ventilated at 24 breaths/minute. The results showed that in these pigs the respiratory component acted as a noise source that blurred the sigmoid-shaped relationship between blood pressure and heart period changes during the baroreceptor function tests.

http://ieeexplore.ieee.org/iel4/6018/16077/00747165.pdf

S. Morrison

Papers

Wavelet decomposition of cardiovascular signals for baroreceptor function tests in pigs

Wavelet decomposition of baroreceptor function tests in pigs