## Abstract

Original language | English |
---|---|

Pages (from-to) | 2628-2638 |

Number of pages | 11 |

Journal | IEEE Transactions on Biomedical Engineering |

Volume | 64 |

Issue number | 11 |

DOIs | |

Publication status | Published - 2017 |

## Keywords

- Autonomic nervous system
- cardiovascular control
- head-down tilt (HDT)
- head-up tilt (HUT)
- heart rate variability
- Wiener-Granger causality
- Entropy
- Cardiovascular control
- Granger Causality
- Head-up tilts
- Heart rate variability
- Redundancy
- adult
- aged
- arterial pressure
- Article
- autonomic nervous system
- breathing
- cardiovascular parameters
- decomposition
- entropy
- extrasystole
- female
- head-down tilt
- heart cycle
- human
- kernel method
- limit of quantitation
- male
- net redundancy
- normal human
- pressoreceptor reflex
- prevalence
- QRS complex
- redundancy analysis
- systolic arterial pressure
- thorax pressure

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*IEEE Transactions on Biomedical Engineering*,

*64*(11), 2628-2638. https://doi.org/10.1109/TBME.2017.2654509

**Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation via Predictability and Transfer Entropy Decomposition Frameworks.** / Porta, A.; Bari, V.; De Maria, B.; Takahashi, A.C.M.; Guzzetti, S.; Colombo, R.; Catai, A.M.; Raimondi, F.; Faes, L.

Research output: Contribution to journal › Article › peer-review

*IEEE Transactions on Biomedical Engineering*, vol. 64, no. 11, pp. 2628-2638. https://doi.org/10.1109/TBME.2017.2654509

**Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation via Predictability and Transfer Entropy Decomposition Frameworks**. In: IEEE Transactions on Biomedical Engineering. 2017 ; Vol. 64, No. 11. pp. 2628-2638.

}

TY - JOUR

T1 - Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation via Predictability and Transfer Entropy Decomposition Frameworks

AU - Porta, A.

AU - Bari, V.

AU - De Maria, B.

AU - Takahashi, A.C.M.

AU - Guzzetti, S.

AU - Colombo, R.

AU - Catai, A.M.

AU - Raimondi, F.

AU - Faes, L.

N1 - Cited By :1 Export Date: 2 March 2018 CODEN: IEBEA Correspondence Address: Porta, A.; Department of Biomedical Sciences for Health, University of MilanItaly; email: alberto.porta@unimi.it References: Faes, L., Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment (2016) Philosoph. Trans. Roy. Soc. Amer., 374; Porta, A., Disentangling cardiovascular control mechanisms during head-down tilt via joint transfer entropy and self-entropy decompositions (2015) Front. Physiol., 6; Faes, L., Information decomposition in bivariate systems: Theory and application to cardiorespiratory dynamics (2015) Entropy, 17, pp. 277-303; Porta, A., Conditional self-entropy and conditional joint transfer entropy in heart period variability during graded postural challenge (2015) PLoS ONE, 10; Angelini, L., Redundant variables and Granger causality (2010) Phys. Rev. e, 81; Stramaglia, S., Synergy and redundancy in the Granger causal analysis of dynamical networks (2014) New J. Phys., 16; Barrett, A.B., Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems (2015) Phys. Rev. e, 91; Stramaglia, S., Expanding the transfer entropy to identify information circuits in complex systems (2012) Phys. Rev. e, 86; Cohen, M.A., Taylor, J.A., Short-term cardiovascular oscillations in man: Measuring and modelling the physiologies (2002) J. Physiol., 542, pp. 669-683; Koepchen, H.P., Neurophysiological background of central neural cardiovascular-respiratory coordination: Basic remarks and experimental approach (1981) J. Auton. Nervous Syst., 3, pp. 335-368; Porta, A., Effects of variations of the complexity of the target on the assessment of Wiener-Granger causality in cardiovascular control indexes (2016) Physiol. Meas., 37, pp. 276-290; Goldberger, A.L., Non-linear dynamics for clinicians: Chaos theory, fractals and complexity at the bedside (1996) Lancet, 347, pp. 1312-1314; Porta, A., Causal relationships between heart period and systolic arterial pressure during graded head-up tilt (2011) Amer. J. Physiol., 300, pp. R378-R386; Soderstrom, T., Stoica, P., (1988) System Identification, , Englewood Cliffs, NJ, USA: Prentice Hall; McEliece, R.J., (2002) The Theory of Information and Coding, , Cambridge, U. K. : Cambridge Univ. Press; Porta, A., Are nonlinear model-free conditional entropy approaches for the assessment of cardiac control complexity superior to the linear model-based one (2016) IEEE Trans. Biomed. Eng.; Andrzejak, R.G., Bivariate surrogate techniques: Necessity, strengths, and caveats (2003) Phys. Rev. e, 68; Eckberg, D.L., Temporal response patterns of the human sinus node to brief carotid baroreceptor stimuli (1976) J. Physiol., 258, pp. 769-782; Baselli, G., Model for the assessment of heart period and arterial pressure variability interactions and respiratory influences (1994) Med. Biol. Eng. Comput., 32, pp. 143-152; Porta, A., Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series (2012) IEEE Trans. Biomed. Eng., 59 (3), pp. 832-841. , Mar; Porta, A., Model-based assessment of baroreflex and cardiopulmonary couplings during graded head-up tilt (2012) Comput. Biol. Med., 42, pp. 298-305; Baselli, G., Spectral decomposition in multichannel recordings based on multivariate parametric identification (1997) IEEE Trans. Biomed. Eng., 44 (11), pp. 1092-1101. , Nov; Akaike, H., A new look at the statistical novel identification (1974) IEEE Trans. Autom. Control, 19 (6), pp. 716-723. , Dec; Faes, L., Mutual non linear prediction as a tool to evaluate coupling strength and directionality in bivariate time series: Comparison among different strategies based on k nearest neighbors (2008) Phys. Rev. e, 78; Harder, M., Bivariate measure of redundant information (2013) Phys. Rev. e, 87; Griffith, V., Intersection information based on common randomness (2014) Entropy, 16, pp. 1985-2000; Bertschinger, N., Quantifying unique information (2014) Entropy, 16, pp. 2161-2183; Xiao, X., System identification: Amulti-signal approach for probing neural cardiovascular regulation (2005) Physiol. Meas., 26, pp. R41-R71; Cooke, W.H., Human responses to upright tilt: A window on central autonomic integration (1999) J. Physiol., 517, pp. 617-628; Montano, N., Power spectrum analysis of heart rate variability to assess changes in sympatho-vagal balance during graded orthostatic tilt (1994) Circulation, 90, pp. 1826-1831; Porta, A., Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability (2007) Amer. J. Physiol., 293, pp. H702-H708; Marchi, A., Calibrated variability of muscle sympathetic nerve activity during graded head-up tilt in humans and its link with noradrenaline data and cardiovascular rhythms (2016) Amer. J. Physiol., 310, pp. R1134-R1143; Furlan, R., Oscillatory patterns in sympathetic neural discharge and cardiovascular variables during orthostatic stimulus (2000) Circulation, 101, pp. 886-892; Faes, L., Investigating the mechanisms of cardiovascular and cerebrovascular regulation in orthostatic syncope through an information decomposition strategy (2013) Auton. Neurosci., Basic Clin., 178, pp. 76-82; Heart rate variability-Standards of measurement, physiological interpretation and clinical use (1996) Circulation, 93, pp. 1043-1065. , Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology; Porta, A., Short-term complexity indexes of heart period and systolic arterial pressure variabilities provide complementary information (2012) J. Appl. Physiol., 113, pp. 1810-1820; Saul, J.P., Transfer function analysis of the circulation: Unique insights into cardiovascular regulation (1991) Amer. J. Physiol., 261, pp. H1231-H1245; Mullen, T.J., System identification of closed loop cardiovascular control: Effects of posture and autonomic blockade (1997) Amer. J. Physiol., 272, pp. H448-H461; Nikias, L., Raghuveer, M.R., Bispectrum estimation: A digital signal processing framework (1987) Proc. IEEE, 75 (7), pp. 869-891. , Jul; Nakamura, T., Multiscale analysis of intensive longitudinal biomedical signals and its clinical applications (2016) Proc. IEEE, 104 (2), pp. 242-261. , Feb; Peng, C.-K., Long-range anticorrelations and non-Gaussian behavior of the heartbeat (1993) Phys. Rev. Lett., 70, pp. 1343-1346; Porta, A., Faes, L., Wiener-Granger causality in network physiology with applications to cardiovascular control and neuroscience (2016) Proc. IEEE, 104 (2), pp. 282-309. , Feb; Timme, N., Synergy, redundancy, andmultivariate informationmeasures: An experimentalist's perspective (2014) J. Comput. Neurosci., 36, pp. 119-140; Papana, A., Simulation study of direct causality measures in multivariate time series (2013) Entropy, 15, pp. 2635-2661; Faes, L., Linear and nonlinear analysis of brain-heart and brain-brain interactions during sleep (2015) Phys. Meas., 36, pp. 683-698; Voss, A., The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death (1996) Cardiovasc. Res., 31, pp. 419-433; Wessel, N., Short-term forecasting of life-threatening cardiac arrhythmias based on symbolic dynamics and finite-time growth rates (2000) Phys. Rev. e, 61, pp. 733-739; Barrett, A.B., Multivariate Granger causality and generalized variance (2010) Phys. Rev. e, 81; Muller, A., Causality in physiological signals (2016) Physiol. Meas., 37, pp. R46-R72

PY - 2017

Y1 - 2017

N2 - Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy/synergy indexes were derived according to predictability and transfer entropy decomposition strategies via a multivariate linear regression approach. Indexes were tested in two protocols inducing modifications of the cardiovascular regulation via baroreflex loading/unloading (i.e., head-down tilt at -25° and graded head-up tilt at 15°, 30°, 45°, 60°, 75°, and 90°, respectively). The net redundancy/synergy of SAP and R to HP and of HP and R to SAP were estimated over stationary sequences of 256 successive values. Results: We found that: 1) regardless of the target (i.e., HP or SAP) redundancy was prevalent over synergy and this prevalence was independent of type and magnitude of the baroreflex challenge; 2) the prevalence of redundancy disappeared when decoupling inputs from output via a surrogate approach; 3) net redundancy was under autonomic control given that it varied in proportion to the vagal withdrawal during graded head-up tilt; and 4) conclusions held regardless of the decomposition strategy. Conclusion: Net redundancy indexes can monitor changes of cardiovascular control from a perspective completely different from that provided by more traditional univariate and multivariate methods. Significance: Net redundancy measures might provide a practical tool to quantify the reservoir of effective cardiovascular regulatory mechanisms sharing causal influences over a target variable. © 1964-2012 IEEE.

AB - Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy/synergy indexes were derived according to predictability and transfer entropy decomposition strategies via a multivariate linear regression approach. Indexes were tested in two protocols inducing modifications of the cardiovascular regulation via baroreflex loading/unloading (i.e., head-down tilt at -25° and graded head-up tilt at 15°, 30°, 45°, 60°, 75°, and 90°, respectively). The net redundancy/synergy of SAP and R to HP and of HP and R to SAP were estimated over stationary sequences of 256 successive values. Results: We found that: 1) regardless of the target (i.e., HP or SAP) redundancy was prevalent over synergy and this prevalence was independent of type and magnitude of the baroreflex challenge; 2) the prevalence of redundancy disappeared when decoupling inputs from output via a surrogate approach; 3) net redundancy was under autonomic control given that it varied in proportion to the vagal withdrawal during graded head-up tilt; and 4) conclusions held regardless of the decomposition strategy. Conclusion: Net redundancy indexes can monitor changes of cardiovascular control from a perspective completely different from that provided by more traditional univariate and multivariate methods. Significance: Net redundancy measures might provide a practical tool to quantify the reservoir of effective cardiovascular regulatory mechanisms sharing causal influences over a target variable. © 1964-2012 IEEE.

KW - Autonomic nervous system

KW - cardiovascular control

KW - head-down tilt (HDT)

KW - head-up tilt (HUT)

KW - heart rate variability

KW - Wiener-Granger causality

KW - Entropy

KW - Cardiovascular control

KW - Granger Causality

KW - Head-up tilts

KW - Heart rate variability

KW - Redundancy

KW - adult

KW - aged

KW - arterial pressure

KW - Article

KW - autonomic nervous system

KW - breathing

KW - cardiovascular parameters

KW - decomposition

KW - entropy

KW - extrasystole

KW - female

KW - head-down tilt

KW - heart cycle

KW - human

KW - kernel method

KW - limit of quantitation

KW - male

KW - net redundancy

KW - normal human

KW - pressoreceptor reflex

KW - prevalence

KW - QRS complex

KW - redundancy analysis

KW - systolic arterial pressure

KW - thorax pressure

U2 - 10.1109/TBME.2017.2654509

DO - 10.1109/TBME.2017.2654509

M3 - Article

VL - 64

SP - 2628

EP - 2638

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 11

ER -