Short-term heart rate variability—age dependence in healthy subjects

Abstract

Heart rate variability (HRV) analysis is an established method to characterize the autonomic regulation and is based mostly on 24h Holter recordings. The importance of short-term HRV (less than 30 min) for various applications is growing consistently. Major reasons for this are the suitability for ambulatory care and patient monitoring and the ability to provide an almost immediate test result. So far, there have been only a few studies that provided statistically relevant reference values for short-term HRV. In our study, 5 min short-term HRV indices were determined from 1906 healthy subjects. From these records, linear and nonlinear indices were extracted. To determine general age-related influences, HRV indices were compared from subjects aged 25-49 years with subjects aged 50-74 years. In a second approach, we examined the development of HRV indices by age in terms of age decades (25-34, 35-44, 45-54, 55-64 and 65-74 years). Our results showed significant variations of HRV indices by age in almost all domains. While marked dynamics in terms of parameter change (variability reduction) were observed in the first age decades, in particular the last two age decades showed certain constancy with respect to the HRV indices examined.

Increased prevalence of cardiac autonomic dysfunction at different degrees of glucose intolerance in the general population: the KORA S4 survey

“…A total number of 120 HRV variables (time domain, 15 indices; frequency domain, 15 indices; NLD, 90 indices using eight different methods) were determined by applying linear and nonlinear HRV analysis methods to the filtered tachograms. Calculations of the HRV indices were performed containing an in-house software package [34][35][36].…”

“…Multivariate linear regression analysis was performed to examine which risk factors are independently associated with the indices of HRV and QT variability as dependent variables. The latter were transformed using the Yeo-Johnson transformation [35] to account for skewness and approximate normality, followed by a stepwise model selection, starting with the full model and minimising the Akaike information criterion. Independent variables in the full model included age, sex, BMI, waist circumference, heart rate, physical activity, smoking, alcohol intake, dietary habits, hypertension, cardiovascular disease, drugs potentially increasing or decreasing HRV and those without a clear effect on HRV (3 groups), glucose tolerance status, HbA 1c , serum triacylglycerols, HDL-cholesterol, LDL-cholesterol, creatinine and uric acid.…”

Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

“…The authors reported significantly different entropy characteristics over multiple time scales for aged than for younger subjects [37]. Reduced heart rate variability in elderly subjects is well known and has been hypothesized to be caused by reduced vagal heart rate modulations [47]. However, from the best of our knowledge, compression entropy on single or multiple time scales has never been studied nor on LSCI data nor on LDF time series.…”

Informative Nature and Nonlinearity of Lagged Poincaré Plots Indices in Analysis of Heart Rate Variability

“…We used short-term (5-min) HRV indices analysis in both linear time and frequency domains and in the nonlinear dynamics domain [45]. The electrocardiogram (ECG) was recorded while the patients were in a sitting position after resting for at least 20 min.…”