ARFIMA-GARCH modeling of HRV: Clinical application in acute brain injury

In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The ARFIMA-GARCH modeling considered here allows the quantification of long range correlations and time-varying volatility. ARFIMA-GARCH HRV analysis is integrated with multimodal brain monitoring in several acute cerebral phenomena such as intracranial hypertension, decompressive craniectomy and brain death. The results indicate that ARFIMA-GARCH modeling appears to reflect changes in Heart Rate Variability (HRV) dynamics related both with the Acute Brain Injury (ABI) and the medical treatments effects. (c) 2017, Springer International Publishing AG

The sensitivity of 38 heart rate variability measures to the addition of artifact in human and artificial 24-hr cardiac recordings

Background: Artifact is common in cardiac RR interval data derived from 24-hr recordings and has a significant impact on heart rate variability (HRV) measures. However, the relative impact of progressively added artifact on a large group of commonly used HRV measures has not been assessed. This study compared the relative sensitivity of 38 commonly used HRV measures to artifact to determine which measures show the most change with increasing increments of artifact. A secondary aim was to ascertain whether short-term and long-term HRV measures, as groups, share similarities in their sensitivity to artifact. Methods: Up to 10% of artifact was added to 20 artificial RR (ARR) files and 20 human cardiac recordings, which had been assessed for artifact by a cardiac technician. The added artifact simulated deletion of RR intervals and insertion of individual short RR intervals. Thirty-eight HRV measures were calculated for each file. Regression analysis was used to rank the HRV measures according to their sensitivity to artifact as determined by the magnitude of slope. Results: RMSSD, SDANN, SDNN, RR triangular index and TINN, normalized power and relative power linear measures, and most nonlinear methods examined are most robust to artifact. Conclusion: Short-term time domain HRV measures are more sensitive to added artifact than long-term measures. Absolute power frequency domain measures across all frequency bands are more sensitive than normalized and relative frequency domain measures. Most nonlinear HRV measures assessed were relatively robust to added artifact, with Poincare plot SD1 being most sensitive.No Full Tex

To what extent it is possible to predict falls due to standing hypotension by using HRV and wearable devices? Study design and preliminary results from a proof-of-concept study

Falls are a major problem in later life reducing the well-being, mobility and quality of life. One of the main causes of falls is standing hypotension. This paper presents the design and the very preliminary results of a pilot study aiming to investigate if it is possible to predict standing hypotension and in projection those falls due to standing hypotension, using the HRV short term recording to estimate the blood pressure drop-down ((BP) due to fast rising up from a bed. The preliminary results shown that in the 79% of the experiment conducted, the HRV acquired with commercial wearable devices could predict (BP due to standing hypotension with an error below the sphigmomanoter measurement error

An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis*

Heart rate variability (HRV) analysis is affected by ectopic beats. An efficient method was proposed to deal with the ectopic beats. The method was based on trend correlation of the heart timing signal. Predictor of R-R interval (RRI) value at ectopic beat time was constructed by the weight calculation and the slope estimation of preceding normal RRI. The type of ectopic beat was detected and replaced by the predictor of RRI. The performance of the simulated signal after ectopic correction was tested by the standard value using power spectrum density (PSD) estimation, whereas the results of clinical data with ectopic beats were compared with the adjacent ectopic-free data. The result showed the frequency indexes after ectopy corrected had less error than other methods with the test of simulated signal and clinical data. It indicated our method could improve the PSD estimation in HRV analysis. The method had advantages of high accuracy and real time properties to recover the sinus node modulation

Time-varying cardiovascular complexity with focus on entropy and lyapunov exponents

Measures of nonlinearity and complexity, such as entropy measures and Lyapunov exponents, have been increasingly employed to characterize dynamical properties in a wide range of biological nonlinear systems, including cardiovascular control. In this chapter, we present recent methodological advances allowing to effectively estimate instantaneous approximate and sample entropy measures, as well as the instantaneous Lyapunov spectrum of a series of stochastic events, i.e., the heartbeats. Because the proposed measures are instantaneously defined by means of probability functions defined within a point-process framework, these indices are able to provide instantaneous tracking of the degree of complexity associated with the physiological system in question. Long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications on experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure) are reported