Heart rate variability: a telltale of health or disease

International Journal of Bio-Medical Computing, 1989

Reduced heart rate variability has been reported as a predictor of long-term mortality in recent myocardial infarction patients. However, it has not been systematically investigated whether the reduction in heart rate variability in those post myocardial infarction patients who later suffer death or severe arrhythmias is caused by a reduction of short-term variability of heart rate (such as respiratory arrhythmia) or whether the differences in long term variability (such as diurnal rhythm) are involved. In order to perform such an evaluation, a new algorithm has been developed which permits different wavelength components (including the long-term components due to diurnal rhythm) of heart rate variability to be approximated. In general, the method uses segmental frequency distributions of durations of intervals between successive normal cardiac beats. To assess the spectral components of heart rate variability, a scale of wavelength limits is used and for each limit of this scale, the algorithm excludes the rate changes of wavelength longer than the given bound. The method was applied to the analysis of electrocardiograms recorded in 14 post myocardial infarction patients who later suffered death or ventricular tachycardia, and in 14 other randomly selected patients with an uncomplicated course following acute myocardial infarction. The rate variability spectra obtained for both groups of patients were compared statistically and the results showed that the groups of positive and negative cases were most signilicantly distinguished when including both short-and long-term components of heart rate variability. Separate evaluation of different wavelength components showed that the very long-term components of heart rate variability were more powerful in distinguishing between positive and negative cases than the short term components.

2009

Among the techniques used in its evaluation, the heart rate variability (HRV) has arising as a simple and non-invasive measure of the autonomic impulses, representing one of the most promising quantitative markers of the autonomic balance. The HRV describes the oscillations in the interval between consecutive heart beats (RR interval), as well as the oscillations between consecutive instantaneous heart rates. It is a measure that can be used to assess the ANS modulation under physiological conditions, such as wakefulness and sleep conditions, different body positions, physical training and also pathological conditions. Changes in the HRV patterns provide a sensible and advanced indicator of health involvements. Higher HRV is a signal of good adaptation and characterizes a health person with efficient autonomic mechanisms, while lower HRV is frequently an indicator of abnormal and insufficient adaptation of the ANS, provoking poor patient's physiological function. Because of its importance as a marker that reflects the autonomic nervous system activity on the sinus node and as a clinical instrument to assess and identify health involvements, this study reviews conceptual aspects of the HRV, measurement devices, filtering methods, indexes used in the HRV analyses, limitations in the use and clinical applications of the HRV.

Frontiers in physiology, 2015

Cardiovascular Research

Objective: To obtain data relating to the reproducibility of the time and frequency domain measurements obtained from IO-mm ECG recordings. Methods: Eighteen normal volunteers underwent evaluations of time and frequency domain heart rate variability 2 weeks and 7 months after baseline analysis. The time domain parameters were mean NN, the standard deviation of NN intervals, the percentage of successive NN intervals > 50 ms and the root mean square successive difference of NN intervals. The frequency domain evaluations (total power, low frequency, and high frequency) were made by means of both the Fast Fourier Transform algorithm (FIT') and the autoregressive method (AR) from IO-mm ECG recordings made under three different conditions: rest, controlled respiration, and after a passive head-up tilt test. Reproducibility was evaluated by means of the interclass correlation coefficient (ICC), comparing baseline values with the results obtained at the second week and the seventh month. Time domain evaluation were also made from lo-min ECG.

Heart, 1997

Frontiers in Neurology

Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1-24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.

Ann Noninvasive Electrocardio, 1996

arXiv (Cornell University), 2012

International Journal of Cardiology, 2013

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.