scholarly journals Multiscale assessment of the degree of multifractality for physiological time series

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
Andrea Faini ◽  
Gianfranco Parati ◽  
Paolo Castiglioni
Keyword(s):  
Heart Rate ◽  
Random Processes ◽  
Fluctuation Analysis ◽  
Singularity Spectrum ◽  
Theme Issue ◽  
Spectrum Width ◽  
Challenges And Opportunities ◽  
Cumulative Function ◽  
New Challenges ◽  
Detrended Fluctuation

Recent advancements in detrended fluctuation analysis (DFA) allow evaluating multifractal coefficients scale-by-scale, a promising approach for assessing the complexity of biomedical signals. The multifractality degree is typically quantified by the singularity spectrum width ( W SS ), a method that is critically unstable in multiscale applications. Thus, we aim to propose a robust multiscale index of multifractality, compare it with W SS and illustrate its performance on real biosignals. The proposed index is the cumulative function of squared increments between consecutive DFA coefficients at each scale n : α CF ( n ). We compared it with W SS calculated scale-by-scale considering monofractal/monoscale, monofractal/multiscale, multifractal/monoscale and multifractal/multiscale random processes. The two indices provided qualitatively similar descriptions of multifractality, but α CF ( n ) differentiated better the multifractal components from artefacts due to crossovers or detrending overfitting. Applied on 24 h heart rate recordings of 14 participants, the singularity spectrum failed to always satisfy the concavity requirement for providing meaningful W SS , while α CF ( n ) demonstrated a statistically significant heart rate multifractality at night in the scale ranges 16–100 and 256–680 s. Furthermore, α CF ( n ) did not reject the hypothesis of monofractality at daytime, coherently with previous reports of lower nonlinearity and monoscale multifractality during the day. Thus, α CF ( n ) appears a robust index of multiscale multifractality that is useful for quantifying complexity alterations of physiological series. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
D. Nuzzi ◽  
S. Stramaglia ◽  
M. Javorka ◽  
D. Marinazzo ◽  
A. Porta ◽  
...  
Keyword(s):  
Heart Rate ◽  
Granger Causality ◽  
Sampling Rate ◽  
Respiratory Rhythm ◽  
Control Mechanisms ◽  
Theme Issue ◽  
Challenges And Opportunities ◽  
Speed Up ◽  
Suitable Treatment ◽  
New Challenges

Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of ‘instantaneous’ effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index of undirected instantaneous causality and (ii) a novel measure of GC including instantaneous effects. An effective procedure to speed up the optimization of parameters in this frame is also presented. After illustrating the proposed formalism in a theoretical example, we apply it to two datasets of cardiovascular and respiratory time series and compare the values obtained within the frequency bands of physiological interest by the proposed total measure of causality with those derived from the standard GC analysis. We find that the inclusion of instantaneous causality allows us to correctly disentangle the baroreflex mechanism from the effects related to cardiorespiratory interactions. Moreover, studying how controlling the respiratory rhythm acts on cardiovascular interactions, we document an increase of the direct (non-baroreflex mediated) influence of respiration on the heart rate in the respiratory frequency band when switching from spontaneous to paced breathing. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

The effect of respiratory oscillations in heart rate on detrended fluctuation analysis

2017 ◽  
Vol 90 (10) ◽  
Author(s):  
Rathinaswamy B. Govindan ◽  
Srinivas Kota ◽  
Tareq Al-Shargabi ◽  
Christopher B. Swisher ◽  
Adre du Plessis
Keyword(s):  
Heart Rate ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis of Heart Rate Variability in Noise Exposure

2014 ◽  
Vol 1044-1045 ◽  
pp. 1129-1134 ◽  
Author(s):  
Shih Tsung Chen ◽  
Li Ho Tseng ◽  
Yuan Po Lee ◽  
Hong Zhun Wu ◽  
Chia Yi Chou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Detrended Fluctuation Analysis ◽  
Low Frequency ◽  
Fluctuation Analysis ◽  
Frequency Noise ◽  
Cardiovascular Activity ◽  
Low Frequency Noise ◽  
Rr Intervals ◽  
Detrended Fluctuation

During the past two decades, most studies have employed questionnaires to characterize the effects of noise on behavior and health. Developments in physiological techniques have provided a noninvasive method for recording cardiovascular autonomic activity by using an electrocardiogram (ECG). We investigated cardiovascular activity changes in exposure to exposure to low-frequency noise for various noise intensities by using detrended fluctuation analysis (DFA) of heart rate variability (HRV). We hypothesized that distinct noise intensities would affect cardiovascular activity, which would be reflected in the HRV and DFA parameters. A total of 17 healthy volunteers participated in this study. The test intensities of noises were no noise, 70-dBC, 80-dBC, and 90-dBC. Each noise was sustained for 5 minutes and the ECG was recorded simultaneously. The cardiovascular responses were evaluated using DFA of the beat-to-beat (RR) intervals obtained from ECG signals. The results showed that the mean RR intervals variability and mean blood pressure did not substantially change relative to the noises. However, the short-term scaling exponent (α1) of the DFA of the background noise (no noise) condition was lower than the 70-dBC, 80-dBC and 90-dBC noises (P< 0.05, repeated measures analysis of variance). The α1of 90-dBC noise was significantly higher than the α1of BN condition according to a Mann–Whitney U test (P< 0.01). We concluded that exposure to low-frequency noise significantly affects the temporal correlations of HRV, but it does not influence RR intervals variability.

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