Heart rate variability analysis in general medicine

Journal of the Intensive Care Society, 2019

Variation in the time interval between consecutive R wave peaks of the QRS complex has long been recognised. Measurement of this RR interval is used to derive heart rate variability. Heart rate variability is thought to reflect modulation of automaticity of the sinus node by the sympathetic and parasympathetic components of the autonomic nervous system. The clinical application of heart rate variability in determining prognosis post myocardial infarction and the risk of sudden cardiac death is well recognised. More recently, analysis of heart rate variability has found utility in predicting foetal deterioration, deterioration due to sepsis and impending multiorgan dysfunction syndrome in critically unwell adults. Moreover, reductions in heart rate variability have been associated with increased mortality in patients admitted to the intensive care unit. It is hypothesised that heart rate variability reflects and quantifies the neural regulation of organ systems such as the cardiovasc...

Scandinavian Journal of Work, Environment & Health, 1995

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.

Canadian Journal of Anesthesia/Journal canadien d'anesthésie, 2008

higher fluctuations of R-R intervals for the slow average HR than for the fast one. Moreover, the fluctuations of R-R intervals for fast HR may not be as high as for slow HR because the R-R intervals should have become negative (Fig. ) . Due to these facts, standard HRV analysis may be mathematically biased, particularly if patients exhibit different average HRs. To overcome this problem, one should calculate the variability of R-R intervals with respect to the average R-R interval, i.e. normalise the oscillations with respect to the mean value. One can do that by dividing the sequence of R-R intervals by the corresponding average R-R interval . Or, one may divide standard deviation of R-R intervals by average R-R interval (i.e. calculation of coefficient of variation), or divide HRV power spectrum (or its components) by the average R-R interval squared . Such a normalisation is critical for investigations of HRV after different interventions which change HR because by employing this approach, one may differentiate between physiologically and mathematically mediated changes in HRV (i.e. one may exclude the mathematical bias) . For example, metoprolol-induced changes of HRV become insignificant after they are normalised to the same R-R interval, suggesting that the increase in HRV after beta-blockade can be explained by a change of HR . Also, in an animal model, it has been shown that beta-adrenergic receptor blockade may reduce rather than increase R-R interval variability after correction for the drug-induced HR reductions . Furthermore, an employment of this correction method has helped to demonstrate that HR is a better indicator of higher fitness than its variability -i.e. an association between HRV indices and maximal oxygen intake (VO 2 max) exists mainly due to the relationship between HR and VO 2 max . On the other hand, the same method has shown that an increase in HRV following dengue viral infection does not result from the accompanying reduction in HR, but reflects a real improvement in cardiac autonomic nervous control . Therefore, it is necessary to establish to what extent HRV changes associated with HR alterations are physiologically and

Journal of The American College of Cardiology, 1999

The objectives of this review are to discuss the diversity of mechanisms that may explain the association between heart rate (HR) variability and mortality, to appraise the clinical applicability of traditional and new measures of HR variability and to propose future directions in this field of research.

Progress in Cardiovascular Diseases, 2012

Heart rate variability (HRV) non-invasively assesses the activity of the autonomic nervous system. During the past 30 years, an increasing number of studies have related the imbalance of the autonomic nervous system (as assessed by HRV) to several pathophysiogical conditions, particularly in the setting of cardiovascular disease. Sudden death, coronary artery disease, heart failure, or merely cardiovascular risk factors (smoking, diabetes, hyperlipidemia, and hypertension) are the best-known clinical circumstances that can affect and/or be affected by the autonomic nervous system. Analyses of HRV variables have been proposed as a component of the clinical evaluation for patient risk stratification due to its independent prognostic information. Yet the potential for HRV to be used widely in clinical practice remains to be established.

Frontiers in physiology, 2015

Journal of Clinical Medicine, 2023

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Physiological Measurement, 2020

Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes.

European Heart Journal, 2000

Revista Brasileira De Fisioterapia, 2020

Background: Heart rate variability is used as an assessment method for cardiac autonomic modulation. Since the Task Force's publication on heart rate variability in 1996, the European Heart Rhythm Association Position Paper in 2015 and a recent publication in 2017, attention has been paid to recommendations on using heart rate variability analysis methods, as well as their applications in different physiological conditions and clinical studies. This analysis has proved to be useful as a complementary tool for clinical evaluation and to assess the effect of non-pharmacological therapeutic interventions, such as physical exercise programmes, on cardiac autonomic modulation. Objective: The aim of this article is to make recommendations and to develop a checklist of normalisation procedures regarding the use of heart rate variability data collection and analysis methodology, focusing on the cardiology area and cardiac rehabilitation. Methods: Based on previous heart rate variability publications, this paper provides a description of the most common shortcomings of using the analysis methods and considers recommendations and suggestions on how to minimise these occurrences by using a specific checklist.

Medical & Biological Engineering & Computing, 1992

Ann Noninvasive Electrocardio, 1996

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.