A procedure to correct the effect of heart rate on heart rate variability indices: description and assessment (original) (raw)
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Investigation of the correlation between heart rate and heart rate variability
Computers in Cardiology 1995, 1995
Pathologic conditions are flequently associated with ntarked changes in heart rate (HR), which itself influences ifs variability (HR v). Accordingly, some researchers consider the HR and the HR V to he equivalent measures. Question: Does the HRV afford dvferent information than the HR itself7 Method: The HRV were investigated in ten patients during a normal autonomic tone and a pharmacological autonomic b lockade, respectively. The results were analysed by calculation of the product-nroment correlation coeflcient and by means of linear regression of he HRV and the HR. Results: Signrficant correldion were found between the IIR and all of the HRV bands. The regression coeflcients of the HR and HRI' increased mnrkedy qper a .ympathetic blockade. The extent of the rqression decreased to nearly zero dtrring a total ve,qetnti\*e blockade. C'onclusions: ( I ) ll?ere is a signijicant negative correlation between the HR and the HRV.
Journal of Clinical Medicine, 2021
Heart rate variability (HRV) is a method used to evaluate the presence of cardiac autonomic neuropathy (CAN) because it is usually attributed to oscillations in cardiac autonomic nerve activity. Recent studies in other pathologies suggest that HRV indices are strongly related to mean heart rate, and this does not depend on autonomic activity only. This study aimed to evaluate the correlation between the mean heart rate and the HRV indices in women patients with well-controlled T2DM and a control group. HRV was evaluated in 19 T2DM women and 44 healthy women during basal supine position and two maneuvers: active standing and rhythmic breathing. Time-domain (SDNN, RMSSD, pNN20) and frequency-domain (LF, HF, LF/HF) indices were obtained. Our results show that meanNN, age, and the maneuvers are the main predictors of most HRV indices, while the diabetic condition was a predictor only for pNN20. Given the known reduced HRV in patients with T2DM, it is clinically important that much of th...
Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation
Journal of Clinical Monitoring and Computing, 2013
Heart rate variability (HRV) analysis over very short (\60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.
2005
HRV may be calculated using R-R interval fluctuations (RR) or fluctuations of instantaneous heart rates (IHR) (i.e. 1/RR signal). Because of the non-linear relationship between RR-intervals and heart rates, the relation between HRV and average heart rate (HR) may be different depending on whether RR or IHR is employed. This non-linearity also brings problems concerning comparison of HRV between patients revealing different HR's. Aim: To prove that the type of signal (RR or IHR) determines outcomes when analyzing HRV among patients with different HR's and to investigate whether the normalization procedure or the use of corrected signals remove problems concerning the non-linearity. Method: ECG recordings (512 beats) of 55 patients were considered. RR and IHR were calculated. Both signals were divided by their average values yielding the corrected RR and IHR. HRV spectra were estimated from these 4 kinds of signals. Total power (TP), LF and HF components, LF/HF coefficient and normalized values (i.e. nLF, nHF) were calculated. Results: TP and LF estimated from RR correlated negatively with HR but those calculated from IHR revealed a trend towards a positive correlation-respective correlation coefficients differed, p<0.01. The sign of correlation between HF and HR was the same regardless of whether RR or IHR was applied-correlation coefficients did not statistically differ. The correlations of HR with LF/HF and nLF were significantly positive but with nHF significantly negative in all cases (RR, IHR or corrected RR, IHR). The application of corrected signals yielded very similar results despite different signal origins (RR or IHR). Conclusions: Absolute values of TP and LF may exhibit different relationships with HR depending on whether RR or IHR is applied. HRV parameters calculated from corrected signals, nLF, nHF and LF/HF reveal a consistent relation with HR regardless of whether RR or IHR is used. Thus, the application of correction or normalization procedures removes problems related to the non-linear relationship between R-R intervals and heart rates.
Comparison of automatic and manual methods for analyzing heart rate variability parameters
Sciendo eBooks, 2023
Heart rate variability (HRV) is a simple, non-invasive measure to explore the influence of the autonomic nervous system on the cardiovascular system. The current study aims to validate the results obtained by BIOPAC through the extraction of the ECG signal and measure the HRV in two ways, the first by Acqknowledge software program and the second manually. The levels of agreement and the relationship between the time-domain parameters derived by BIOPAC and the manual calculation were examined using a Pearson product-moment correlation and a Bland-Altman plot. Materials and methods: The ECG signal of lead II was recorded, and then the HRV time-domain was calculated using software named Acqknowledge. To verify the validity of these results, we conducted a comparative study by manually calculating the HRV time-domain, and applied the Bland-Atman plot analysis as this method allows us to be sure of your result in a reliable way. Results: The correlation coefficient r was calculated for each parameter RMSSD (r=0.98), SDNN(r=0.98), PNN50%(r=0.99) and had a positive coefficient between HRV measured. The Bland-Altman plot reflects a good agreement between the measurement’s methods used. Conclusion: The present comparative study shows that the values of the two methods are similar and can be used BIOPAC in wider research, even for diagnosis.
Stability over time of variables measuring heart rate variability in normal subjects
The American Journal of Cardiology, 1991
Roth time and frequency domain measures of heart rate (HR) variability have been used to assess autonomic tone in a variety of clinical conditions. Few studies in normal subjects have been performed to determine the stability of HR variability over time, or the correlation between and within time and frequency domain measures of HR variability.
Heart Rate Variability: Objective Assessment of Autonomic Nervous System
MGM Journal of Medical Sciences, 2016
Heart rate variability (HRV) came into existence by observations of Hon and Lee in 1965 and since then has been a subject of prime importance in medical research. It is derived from changes in RR intervals in a continuous recording of electrocardiogram. Different types of measurements are carried out on these RR intervals in time and frequency domain. Among others, variance, total power, low-frequency (LF) power, high-frequency (HF) power, and LF/HF ratio are frequently used HRV parameters for objective assessment of autonomic function and assessment of several clinical conditions. Poincare plot gives a quick visual impression of HRV. This article describes measurement of all these parameters and their clinical applications.
Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice
Computational and Mathematical Methods in Medicine, 2012
Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism's physiologic and/or pathologic state. Physicians are always interested in predicting patient's risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient's state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice.