### Abstract

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

Pages (from-to) | 1287-1296 |

Number of pages | 10 |

Journal | IEEE Transactions on Biomedical Engineering |

Volume | 64 |

Issue number | 6 |

DOIs | |

Publication status | Published - 2017 |

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### Keywords

- Autonomic nervous system
- Cardiovascular regulation
- Head-up tilt
- Heart rate variability
- Mutual information
- Entropy
- Nonlinear systems
- Head-up tilts
- Mutual informations
- Heart
- calculation
- clinical article
- controlled study
- electrocardiogram
- entropy
- experimental model
- female
- head
- heart cycle
- heart rate variability
- human
- k nearest neighbor
- male
- rest
- statistical model
- supine position
- vagus nerve
- algorithm
- biological model
- comparative study
- computer simulation
- evaluation study
- heart
- heart rate
- heart rate measurement
- nonlinear system
- physiological feedback
- physiology
- procedures
- reproducibility
- sensitivity and specificity
- Algorithms
- Computer Simulation
- Feedback, Physiological
- Heart Rate
- Heart Rate Determination
- Humans
- Linear Models
- Models, Cardiovascular
- Nonlinear Dynamics
- Reproducibility of Results
- Sensitivity and Specificity

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*IEEE Transactions on Biomedical Engineering*,

*64*(6), 1287-1296. https://doi.org/10.1109/TBME.2016.2600160

**Are nonlinear model-free conditional entropy approaches for the assessment of cardiac control complexity superior to the linear model-based one?** / Porta, A.; De Maria, B.; Bari, V.; Marchi, A.; Faes, L.

Research output: Contribution to journal › Article

*IEEE Transactions on Biomedical Engineering*, vol. 64, no. 6, pp. 1287-1296. https://doi.org/10.1109/TBME.2016.2600160

**Are nonlinear model-free conditional entropy approaches for the assessment of cardiac control complexity superior to the linear model-based one?**. In: IEEE Transactions on Biomedical Engineering. 2017 ; Vol. 64, No. 6. pp. 1287-1296.

}

TY - JOUR

T1 - Are nonlinear model-free conditional entropy approaches for the assessment of cardiac control complexity superior to the linear model-based one?

AU - Porta, A.

AU - De Maria, B.

AU - Bari, V.

AU - Marchi, A.

AU - Faes, L.

N1 - Cited By :6 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: Van Leeuwen, P., Heart rate variability in the individual fetus (2013) Auton. Neurosci. Basic Clinical, 178, pp. 24-28; Baumert, M., Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy (2012) Comput. Biol. Med., 42, pp. 347-352; Kaplan, D.T., Aging and the complexity of cardiovascular dynamics (1991) Biophys. J., 59, pp. 945-949; Goldberger, A.L., What is physiologic complexity and how does it change with aging and disease? (2002) Neurobiol. Aging, 23, pp. 23-26; Vaillancourt, D.E., Newell, K.M., Changing complexity in human behaviour and physiology through aging and disease (2002) Neurobiol. Aging, 23, pp. 1-11; Viola, A.U., Short-term complexity of cardiac autonomic control during sleep: REM as a potential risk factor for cardiovascular system in aging (2011) PLoS ONE, 6; Porta, A., Effect of age on complexity and causality of the cardiovascular control: Comparison between model-based and model-free approaches (2014) PLoS ONE, 9; Catai, A.M., Effect of the postural challenge on the dependence of the cardiovascular control complexity on age (2014) Entropy, 16, pp. 6686-6704; Pincus, S.M., Heart rate control in normal and aborted-SIDS infants (1993) Amer. J. Physiol., 33, pp. R638-R646; Javorka, M., Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus (2008) Clin. Neurophysiol., 119, pp. 1071-1081; Schulz, S., The altered complexity of cardiovascular regulation in depressed patients (2010) Physiol. Meas., 31, pp. 303-321; Voss, A., Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders (2006) Bipolar Disorder, 8, pp. 441-452; Khandoker, A.H., Identifying diabetic patients with cardiac autonomic neuropathy by heart rate compelxity analysis (2009) Biomed. Eng. Online, 8; Bari, V., Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long QT syndrome type 1 (2014) PLoS ONE, 9; Turianikova, Z., The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure (2011) Physiol. Meas., 32, pp. 1425-1437; Porta, A., Entropy, entropy rate and pattern classification as tools to typify complexity in short heart period variability series (2001) IEEE Trans. Biomed. Eng., 48 (11), pp. 1282-1291. , Nov; Porta, A., Progressive decrease of heart period variability entropybased complexity during graded head-up tilt (2007) J. Appl. Physiol., 103, pp. 1143-1149; Porta, A., Disentangling cardiovascular control mechanisms during head-down tilt via joint transfer entropy and self-entropy decompositions (2015) Front. Physiol., 6; Baumert, M., Entropy analysis of RR and QT interval variability during orthostatic and mental stress in healthy subjects (2014) Entropy, 16, pp. 6384-6393; Lewis, M.J., Short, A.L., Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise (2007) Physiol. Meas., 28, pp. 731-744; Porta, A., Information domain analysis of cardiovascular variability signals: Evaluation of regularity, synchronisation and co-ordination (2000) Med. Biol. Eng. Comput., 38, pp. 180-188; Bar, K.-J., Influence of olanzapine on QT variability and complexity measures of heart rate in patients with schizophrenia (2008) J. Clin. Psychopharmacol., 28, pp. 694-698; Faes, L., Investigating the mechanisms of cardiovascular and cerebrovascular regulation in orthostatic syncope through an information decomposition strategy (2013) Auton. Neurosci. Basic Clinical, 178, pp. 76-82; 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; Lake, D.E., Sample entropy analysis of neonatal heart rate variability (2002) Amer. J. Physiol., 283, pp. R789-R797; Ahmad, S., Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults (2009) PLoS ONE, 4; Takens, F., Detecting strange attractors in fluid turbulence (1981) Dynamical Systems and Turbulence, pp. 366-381. , D. Rand and L. S. Young, Eds. Berlin, Germany: Springer; Richman, J.S., Moorman, J.R., Physiological time-series analysis using approximate entropy and sample entropy (2000) Amer. J. Physiol., 278, pp. H2039-H2049; Pincus, S.M., Approximate entropy (ApEn) as a complexity measure (1995) Chaos, 5, pp. 110-117; Porta, A., Measuring regularity by means of a corrected conditional entropy in sympathetic outflow (1998) Biol. Cybern., 78, pp. 71-78; Porta, A., Complexity and non linearity in short-term heart period variability: Comparison of methods based on local non linear prediction (2007) IEEE Trans. Biomed. Eng., 54 (1), pp. 94-106. , Jan; Porta, A., K-nearest-neighbor conditional entropy approach for the assessment of short-term complexity of cardiovascular control (2013) Physiol. Meas., 34, pp. 17-33; Faes, L., Estimating the decomposition of predictive information in multivariate systems (2015) Phys. Rev. E, 91; Bandt, C., Pompe, B., Permutation entropy: A natural complexity measure for time series (2002) Phys. Rev. Lett., 88; Porta, A., Limits of permutation-based entropies in assessing complexity of short heart period variability (2015) Physiol. Meas., 36, pp. 755-765; Barnett, L., Granger causality and transfer entropy are equivalent for Gaussian variables (2009) Phys. Rev. Lett., 103; Porta, A., An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24h Holter recordings in healthy and heart failure humans (2007) Chaos, 17; Lizier, J.T., Information storage, loop motifs, and clustered structure in complex networks (2012) Phys. Rev. E, 86; Wibral, M., Local active information storage as a tool to understand distributed neural information processing (2014) Front. Neuroinform., 8; Porta, A., Conditional self-entropy and conditional joint transfer entropy in heart period variability during graded postural challenge (2015) PLoS ONE, 10; 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; Akaike, H., A new look at the statistical novel identification (1974) IEEE Trans. Autom. Contr., AC-19 (6), pp. 716-723. , Dec; Hlavackova-Schindler, K., Causality detection based on information-theoretic approaches in time series analysis (2007) Phys. Rep., 441, pp. 1-46; Kozachenko, L.F., Leonenko, N.N., Sample estimate of entropy of a random vector (1987) Problems Inf. Transmiss., 23, pp. 95-100; Kraskov, A., Estimating mutual information (2004) Phys. Rev. E, 69; Wibral, M., Transfer entropy in magnetoencephalographic data: Quantifying information flow in cortical and cerebellar networks (2011) Progr. Biophys. Mol. Biol., 105, pp. 80-97; Lindner, M., TRENTOOL: AMatlab open source toolbox to analyse information flowin time series data with transfer entropy (2011) BMC Neurosci., 12; Wollstadt, P., Efficient transfer entropy analysis of non-stationary neural time series (2013) PLoS ONE, 9; Bassani, T., Model-free causality analysis of cardiovascular variability detects the amelioration of the autonomic control in Parkinson's disease patients undergoing mechanical stimulation (2014) Physiol. Meas., 35, pp. 1397-1408; Runge, J., Escaping the curse of dimensionality in estimating multivariate transfer entropy (2012) Phys. Rev. Lett., 108; 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; Magagnin, V., Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indexes (2011) Physiol. Meas., 32, pp. 1775-1786; Pincus, S.M., Goldberger, A.L., Physiological time-seris analysis: What does regularity quantify? (1994) Amer. J. Physiol., 266, pp. H1643-H1656; Porta, A., Addressing the complexity of cardiovascular regulation (2009) Philosoph. Trans. Roy. Soc. A, 367, pp. 1215-1218; 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; Furlan, R., Oscillatory patterns in sympathetic neural discharge and cardiovascular variables during orthostatic stimulus (2000) Circulation, 101, pp. 886-892; Cooke, W.H., Human responses to upright tilt: A window on central autonomic integration (1999) J. Physiol., 517, pp. 617-628; Marchi, A., Characterization of the cardiovascular control during modified head-up tilt test in healthy adult humans (2013) Auton. Neurosci. Basic Clinical, 179, pp. 166-169; Fortrat, J.O., Respiratory influences on non-linear dynamics of heart rate variability in humans (1997) Biol. Cybern., 77, pp. 1-10; Kanters, K., Influence of forced respiration on nonlinear dynamics in heart rate variability (1997) Amer. J. Physiol., 272, pp. R1149-R1154; Reulecke, S., Men and women should be separately investigated in studies of orthostatic challenge due to different gender-related dynamics of autonomic response (2016) Physiol. Meas., 37, pp. 314-332

PY - 2017

Y1 - 2017

N2 - Objective: We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals. Methods: An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k-nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive Rwave peaks and analysis was carried out over stationary sequences of 256 successive HPs. Results: The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and theMBindexwas remarkably correlated with the MF ones. Conclusion: The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation. Significance: The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications. © 2016 IEEE.

AB - Objective: We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals. Methods: An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k-nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive Rwave peaks and analysis was carried out over stationary sequences of 256 successive HPs. Results: The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and theMBindexwas remarkably correlated with the MF ones. Conclusion: The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation. Significance: The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications. © 2016 IEEE.

KW - Autonomic nervous system

KW - Cardiovascular regulation

KW - Head-up tilt

KW - Heart rate variability

KW - Mutual information

KW - Entropy

KW - Nonlinear systems

KW - Head-up tilts

KW - Mutual informations

KW - Heart

KW - calculation

KW - clinical article

KW - controlled study

KW - electrocardiogram

KW - entropy

KW - experimental model

KW - female

KW - head

KW - heart cycle

KW - heart rate variability

KW - human

KW - k nearest neighbor

KW - male

KW - rest

KW - statistical model

KW - supine position

KW - vagus nerve

KW - algorithm

KW - biological model

KW - comparative study

KW - computer simulation

KW - evaluation study

KW - heart

KW - heart rate

KW - heart rate measurement

KW - nonlinear system

KW - physiological feedback

KW - physiology

KW - procedures

KW - reproducibility

KW - sensitivity and specificity

KW - Algorithms

KW - Computer Simulation

KW - Feedback, Physiological

KW - Heart Rate

KW - Heart Rate Determination

KW - Humans

KW - Linear Models

KW - Models, Cardiovascular

KW - Nonlinear Dynamics

KW - Reproducibility of Results

KW - Sensitivity and Specificity

U2 - 10.1109/TBME.2016.2600160

DO - 10.1109/TBME.2016.2600160

M3 - Article

VL - 64

SP - 1287

EP - 1296

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 6

ER -