Heartbeat Dynamics Analysis under Cold-Pressure Test using Wavelet p-Leader Non-Gaussian Multiscale Expansions

IEEE Trans. Biomed. Eng.

Talebi

Barbieri

et al. 2021

The quantification of functional brainheart interplay through the dynamics of the central and autonomic nervous systems may provide effective biomarkers for cognitive, emotional, and autonomic state changes. Despite several computational models were proposed to this end, none provides a directional estimation of such interplay in a time-resolved and probabilistic fashion. Methods: In this study, a multivariate inhomogeneous point-process model for heartbeat dynamics is employed to derive subject-specific, time-resolved, functional estimates of the directional interplay occurring from the brain to the heart, whose activity is represented by electroencephalography and R-peaks intervals series. An inverse-Gaussian probability density function is used to predict heartbeat events as a function of neural dynamics, which is modeled as an exogenous input to the autoregressive cardiac dynamics. Results: The performance is evaluated using heart rate variability and electroencephalography series gathered from 24 healthy volunteers undergoing a coldpressor test, and the modeling goodness-of-fit is assessed through the time-rescaling theorem. The results suggest that cortical dynamics drives heartbeat series with specific time delays in the range of 30 s to 60 s and 90 s to 120 s from the peripheral thermal stress onset. Conclusion: The proposed framework provides novel insights in human neurophysiology, exploiting a fully probabilistic definition of the continuous functional brain-heart interplay.

Section: Introductionmentioning

confidence: 99%

Time-Resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models

IEEE Trans. Biomed. Eng.

Talebi

Barbieri

et al. 2021

“…The method was tested on experimental EEG and heartbeat series gathered from 24 healthy volunteers undergoing a CPT elicitation that follows a resting state session. Linear and nonlinear features were derived from the cumulants of the MF spectrum of brain and heartbeat series similarly to [25], [26], and their concurrent variation across time-scales was estimated through the MIC. Results suggest that significant statistical differences between the two experimental conditions may be associated with the nonlinear MF feature c 2 , and no differences may be linked to the linear quantifier c 1 .…”

Section: Discussionmentioning

confidence: 99%

“…CPT comprises a strong thermal elicitation with consequent sympathovagal changes and was associated with significantly lower MF features derived from both EEG [26] and heartbeat dynamics [25] than resting state. Furthermore, the crucial role of preprocessing on heartbeat dynamics based on inhomogeneous point-process models has been demonstrated [25], [27].…”

Section: Introductionmentioning

confidence: 96%

“…From an estimation viewpoint, the wavelet transform modulus maxima method [20], and the MF detrended fluctuation analysis [21] were proposed to estimate D(h). Recently, we used a wavelet leader multifractal formalisms [22] and its generalization using p-leaders [23], [24] with non-gaussian expansion to characterize brain and heartbeat dynamics separately during a Cold-Pressor Test (CPT) [25], [26]. CPT comprises a strong thermal elicitation with consequent sympathovagal changes and was associated with significantly lower MF features derived from both EEG [26] and heartbeat dynamics [25] than resting state.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we used a wavelet leader multifractal formalisms [22] and its generalization using p-leaders [23], [24] with non-gaussian expansion to characterize brain and heartbeat dynamics separately during a Cold-Pressor Test (CPT) [25], [26]. CPT comprises a strong thermal elicitation with consequent sympathovagal changes and was associated with significantly lower MF features derived from both EEG [26] and heartbeat dynamics [25] than resting state. Furthermore, the crucial role of preprocessing on heartbeat dynamics based on inhomogeneous point-process models has been demonstrated [25], [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Functional Links between Brain and Heartbeat Dynamics in the Multifractal Domain: a Preliminary Analysis

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

Wendt

Barbieri

et al. 2020

Quantification of brain-heart interplay (BHI) has mainly been performed in the time and frequency domains. However, such functional interactions are likely to involve nonlinear dynamics associated with the two systems. To this extent, in this preliminary study we investigate the functional coupling between multifractal properties of Electroencephalography (EEG) and Heart Rate Variability (HRV) series using a channel-and time scale-wise maximal information coefficient analysis. Experimental results were gathered from 24 healthy volunteers undergoing a resting state and a cold-pressure test, and suggest that significant changes between the two experimental conditions might be associated with nonlinear quantifiers of the multifractal spectrum. Particularly, major brain-heart functional coupling was associated with the secondorder cumulant of the multifractal spectrum. We conclude that a functional nonlinear relationship between brain-and heartbeat-related multifractal sprectra exist, with higher values associated with the resting state.Clinical relevance: The presented methodology could provide biomarkers for supporting the diagnosis of neurological, psychiatric, and cardiovascular disorders.

Heart rate variability in marketing research: A systematic review and methodological perspectives

Kakaria

Bigné

Psychology and Marketing

et al. 2022

Heart rate variability is a promising physiological measurement that accesses psychophysiological variations in response to a marketing stimulus. While its application spans diverse fields, there is a limited understanding of the usability and interpretation of heart rate variability in marketing research. Therefore, this hybrid literature review provides an overview of the emerging use of heart rate variability in marketing research, along with essential methodological considerations. In this context, we blend marketing mix framework with stimulus-organism-response theory, segregating the use of heart rate variability in various marketing research contexts. We follow the preferred reporting items for systematic reviews and metaanalyses (PRISMA) framework to reflect on 33 records obtained from six databases.Our findings suggest that 42% of studies used heart rate variability to investigate promotion-related topics. Overall, heart rate variability is mostly used in combination with Galvanic skin response (48%). Further, 39% of studies used non-portable systems for data collection. Last, using the theory characteristics methodology (TCM) framework, we identified six research avenues: (1) affective, cognitive, and sensorial constructs; (2) personality, thinking style, and demographics; (3) product experience; (4) advertising and branding; (5) correlation with immersive technologies; and (6) triangulation with other neurophysiological tools.