Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain

Luca Faes, Kamil Javorka, Zuzana Turianikova, Zuzana Lazarova, Barbora Czippelova, Jana Krohova, Michal Javorka

Risultato della ricerca: Article

14 Citazioni (Scopus)

Abstract

The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct + indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR → DBP → SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP → RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP → RR interactions, as well as of weaker RR → DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.
Lingua originaleEnglish
pagine (da-a)877-894
Numero di pagine18
RivistaPhysiological Measurement
Volume38
Stato di pubblicazionePublished - 2017

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Blood pressure
Blood Pressure
Time series analysis
Invariance
Electrocardiography
Head
Starlings
Baroreflex

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Physiology (medical)
  • Biomedical Engineering
  • Physiology

Cita questo

Faes, L., Javorka, K., Turianikova, Z., Lazarova, Z., Czippelova, B., Krohova, J., & Javorka, M. (2017). Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain. Physiological Measurement, 38, 877-894.

Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain. / Faes, Luca; Javorka, Kamil; Turianikova, Zuzana; Lazarova, Zuzana; Czippelova, Barbora; Krohova, Jana; Javorka, Michal.

In: Physiological Measurement, Vol. 38, 2017, pag. 877-894.

Risultato della ricerca: Article

Faes, L, Javorka, K, Turianikova, Z, Lazarova, Z, Czippelova, B, Krohova, J & Javorka, M 2017, 'Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain', Physiological Measurement, vol. 38, pagg. 877-894.
Faes L, Javorka K, Turianikova Z, Lazarova Z, Czippelova B, Krohova J e altri. Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain. Physiological Measurement. 2017;38:877-894.
Faes, Luca ; Javorka, Kamil ; Turianikova, Zuzana ; Lazarova, Zuzana ; Czippelova, Barbora ; Krohova, Jana ; Javorka, Michal. / Basic cardiovascular variability signals: Mutual directed interactions explored in the information domain. In: Physiological Measurement. 2017 ; Vol. 38. pagg. 877-894.
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abstract = "The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct + indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR {\^a}†’ DBP {\^a}†’ SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP {\^a}†’ RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP {\^a}†’ RR interactions, as well as of weaker RR {\^a}†’ DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.",
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AU - Faes, Luca

AU - Javorka, Kamil

AU - Turianikova, Zuzana

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AU - Czippelova, Barbora

AU - Krohova, Jana

AU - Javorka, Michal

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N2 - The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct + indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR → DBP → SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP → RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP → RR interactions, as well as of weaker RR → DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.

AB - The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct + indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR → DBP → SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP → RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP → RR interactions, as well as of weaker RR → DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.

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