Feasibility of Ultra-short Term Complexity Analysis of Heart Rate Variability in Resting State and During Orthostatic Stress

Abstract

In this work, we study ultra-short term (UST) complexity of Heart Rate Variability (HRV) and its agreement with analysis of standard short-term (ST) HRV recordings obtained at rest and during orthostatic stress. Conditional Entropy (CE) measures have been computed using both a linear Gaussian approximation and a more accurate model-free approach based on nearest neighbors. The agreement between UST and ST indices has been compared via statistical tests and correlation analysis, suggesting the feasibility of exploiting faster algorithms and shorter time series for detecting changes in cardiovascular control during various states.

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

“…Our results confirmed the feasibility of employing UST series to carry out computation of regularity and complexity measures (see Figs. 4 and 5), already previously reported for CE and Approximate Entropy [31,41,76]. Results evidence that, apart a couple of exceptions, the significant differences between a stressful and the preceding rest condition reported using 300-sample recordings are also retained using shorter series up to 60-sample duration for all the analyzed metrics (SE, DE, CE).…”

“…This finding is difficult to explain, given that the strong nonlinear dynamics contributing to short-term HRV and cardiovascular variability [20,24,69,70] are detected by knn estimator but neglected by model-based parametric approach. The augmented discriminative capability of linear estimator, even if due to the non-linear dynamics present in the phenomenon but not properly taken into account, may be even in perspective used in practical application for a more accurate and fast classification between rest and stress conditions [22,41]. This is also reinforced by the very low computational times required for the lin estimator to compute the entropy-based measures on 300-sample series length (ST standard), which is 24 times lower if compared to knn .…”

“…The present work aims at evaluating the extent to which the loss of physiological information due to UST reduced time series length can be a good tradeoff for extracting physiological indices with lower real-time processing and storage costs, suitably for wearable devices [28,31,36,[38][39][40]. Moreover, while several works have focused on ultra-short term HRV [31,36,40,41], to the best of our knowledge there are no previous studies performing an UST blood pressure variability analysis. Herein, a comparison between UST and ST indices extracted in the time and information domains is performed on a dataset composed of systolic blood pressure (SAP) and inter-beat interval (RR) time series acquired on a population of healthy subjects in rest and when undergoing to orthostatic and mental stress.…”

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

“…Figure 5 (c3) vs. Figure 5 (c1)). The augmented discriminative capability of the linear estimator, even if possibly related to the presence of non-linear dynamics [ 71 , 72 ] which are not properly taken into account, may be a perspective used in practical applications for a more accurate and fast differentiation between rest and stress conditions [ 39 , 67 ]. This is also reinforced by the very low computational times required for the lin estimator to compute the entropy-based measures on 300-sample series length (ST standard), which is 24 times lower if compared to knn .…”

“…The present work aims at evaluating the extent to which the loss of physiological information due to UST reduced time series length can be a good tradeoff for extracting physiological indices with lower real-time processing and storage costs, suitably for wearable devices [ 24 , 25 , 26 , 29 , 34 , 38 ]. Moreover, while several works have focused on ultra-short-term HRV [ 29 , 34 , 38 , 39 ], to the best of our knowledge there are no previous studies performing a UST blood pressure variability analysis. Herein, a comparison between UST and ST indices extracted in the time and information domains is performed on a dataset composed of systolic blood pressure (SAP) and interbeat interval (RR) time series acquired on a population of healthy subjects in rest and when undergoing orthostatic and mental stress.…”