Multiscale analysis of heart rate variability in non-stationary environments

Abstract

Heart rate variability (HRV) is highly non-stationary, even if no perturbing influences can be identified during the recording of the data. The non-stationarity becomes more profound when HRV data are measured in intrinsically non-stationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments. In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA) and scale-dependent Lyapunov exponent (SDLE), for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS) patients and their matched-controls. CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS. Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and non-stationary.

Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China

“…However, AFA presents advantages over rescaled range analysis and detrended fluctuation analysis in managing arbitrary and strong nonlinear trends (Gao et al, 2011;Hu et al, 2009) due to the finer resolution of fractal scaling behavior considered within short time series (Gao et al, 2012) and due to its more accurate Hurst exponent estimations (Gao et al, 2011). Therefore, AFA has been widely used to analyze the persistence and severity of global terrorism trends (Gao et al, 2017), sociocultural phenomena (Gao et al, 2012), electricity power load (Jiang & Gao, 2016), traffic flow (Zhu & Gao, 2014), and bioinformatics patterns (Gao et al, 2011;Gao et al, 2013;Sengupta et al, 2017).…”

Heart-rate variability depression in porcine peritonitis-induced sepsis without organ failure

“…Only the classical time and frequency domain analyses of HRV together with non-linear Poincare plot analysis were employed in this study. Although a number of more advanced non-linear analyses are now available, 30 the signal (HRV reduction) was sufficiently robust to be detected by the classical methods selected.…”

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

“…The study of the complexity of physiological signals, in particular, has led to important results in recent decades in understanding the mechanisms underlying mental illness (Yang and Tsai, 2012 ). Several measures of complexity have also been proposed and applied to the study of mental illness based on various biomedical signals, from EEG (Hu et al, 2006 ; Takahashi et al, 2010 ; Gao et al, 2011 ), to MEG (Fernandez et al, 2010 ), through HRV (Mujica-Parodi et al, 2005 ; Hu et al, 2009 , 2010 ; Gao et al, 2013 ; Valenza et al, 2014b ). Accordingly, in this study we investigate the role of ANS non-linear dynamics in performing the psychological assessment, with respect to the standard analysis, i.e., analysis in the time and frequency domain.…”