The effect of persistent U-shaped patterns in RR night-time series on the heart rate variability complexity in healthy humans

Abstract

Objective: U-shaped patterns, characteristic periods of time observed in tachograms, are a specific subgroup among very low frequency components characterized by relatively short periods of smooth accelerations followed by decelerations of heart rhythm. In this study, we characterize this phenomenon and its effect on heart rate variability (HRV) parameters. Approach: We calculated linear (the mean and standard deviation of RR intervals, RMSSD, pNN50 and the power of the frequency components) and nonlinear (V0, V1 and V2 Porta’s symbolic analysis, Shannon and Sample entropy, Guzik’s and Porta’s asymmetry indexes, the exponents α1 and α2 of detrended fluctuation analysis and the Hurst surface h(q,s) of multiscale multifractal analysis (MMA)) HRV parameters for 65 RR interval night-time series (39 females, 37.5(11.3) years old and 26 males, 41.7(16.5) years old; all without organic heart diseases). All parameters were calculated for original data and for the three kinds of test data in which the following parts of the time series were replaced by 1/f noise: (A) the U-shape patterns annotated in a given data set, (B) randomly chosen windows of similar size as the U-shaped patterns, (C) acceleration-deceleration events shorter than U-shaped patterns. Main results: We found that the U-shaped patterns, as the most persistent structures in RR night-time intervals series, affect the long-range correlation properties (measured by α2). We also found that the U-shaped patterns importantly strictly affect the shape of h(q,s) surface at different scales s. Removing the U-shaped patterns results in the shape of the h(q,s) surface losing the properties characteristic for healthy heart rhythm. The largest quantitative effect of U-shaped patterns was obtained for the power of the VLF component. The mean percentage difference of the VLF component between the original data and the A to C type test data were 19.4%, −4.3% and 5.3%, respectively. Significance: Although percentage contribution of U-shaped patterns is small compared to the whole night-time series (on the average 3.1%(1.7%) with a standard deviation of 1.7%), these patterns have a considerable impact on the HRV parameters describing the VLF, persistency, nonlinear correlations and multifractal properties.

Persistent homology as a new method of the assessment of heart rate variability

“…The persistent homology method has some similarities with the analysis of the statistics of so-called U-shape patterns (see [55,56] and references therein). U-shapes are parts of the tachogram in the night-time recordings consisting of relatively short periods of smooth accelerations followed by decelerations in the heart rhythm, lasting in total approximately 20-40 seconds.…”

“…U-shapes are parts of the tachogram in the night-time recordings consisting of relatively short periods of smooth accelerations followed by decelerations in the heart rhythm, lasting in total approximately 20-40 seconds. So-called short acceleration-deceleration events [56] show some similarities with Ushape patterns but could not be classified as such due to their shorter duration, symmetric acceleration, or smaller relative amplitude. Persistent homology methods provide a more general approach in which each U-shape or short acceleration-deceleration event is related to a persisting connected component of the graph built from the RR data (a persistence interval).…”

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

“…Ultrasonography can be augmented with ultrasound shear wave elastography (SWE) that enable continuous measurement of viscoelastic properties of musculoskeletal tissues [166]. SWE can be used to study the muscle sti-ness after training session [830], Achilles tendon stiness in natural environments using a wearable SWE [302], Achilles tendon stiness and its eect on ROM in squat [270], dynamical muscle contraction during squat [571] with deep learning inversion [224], changes in viscoelastic properties of muscle during fatigue [119], and ecacy of stretch- [756,877]. From these measurements, heart rate variability (HRV) is the most commonly derived parameter [193,389], with the ring or wrist band monitor/sensor oering ease-of-…”

U-shaped Patterns in HRV From a Polysomnographic Point of View: a Quantitative Analysis

“…U-shaped patterns were widely observed in healthy humans. Previous studies showed that these patterns have a considerable impact on the HRV parameters describing the VLF component, persistency, nonlinear correlations and multifractal properties, although their percentage contribution is small compared to the whole night-time series (on the average 3.1±1.7%) [1,3].…”

“…They are, in a domain of RR time intervals, the most laminar structures in nightrime recordings. They are defined as accelerationdeceleration periods in RR interval series, with duration between 20-40 s, and amplitude at smaller at least than 85% of the mean RR interval [1,2].…”