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CC BY-NC-ND 4.0 · Sleep Sci 2022; 15(S 01): 215-223
DOI: 10.5935/1984-0063.20220022
ORIGINAL ARTICLES

Sleep physiological network analysis in children

Authors

  • Alvaro David Orjuela-Cañón

    1   Universidad del Rosario, School of Medicine and Health Sciences - Bogota D.C. - Bogota D.C. - Colombia.

  • Andrés Leonardo Jutinico

    2   Universidad Antonio Nariño, Facultad de Ingeniería Mecánica, Electrónica y Biomédica - Bogota D.C. - Bogota D.C. -Colombia.

  • Maria Angelica Bazurto-Zapata

    3   Fundación Neumológica Colombiana, Laboratorio del Sueño - Bogota D.C. -Bogota D.C. - Colombia.

  • Elida Duenas-Meza

    3   Fundación Neumológica Colombiana, Laboratorio del Sueño - Bogota D.C. -Bogota D.C. - Colombia.
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Objective Physiological networks have recently been employed as an alternative to analyze the interaction of the human body. Within this option, different systems are analyzed as nodes inside a communication network as well how information fows. Several studies have been proposed to study sleep subjects with the help of the Granger causality computation over electroencephalographic and heart rate variability signals. However, following this methodology, novel approximations for children subjects are presented here, where comparison between adult and children sleep is followed through the obtained connectivities.

Methods Data from ten adults and children were retrospectively extracted from polysomnography records. Database was extracted from people suspected of having sleep disorders who participated in a previous study. Connectivity was computed based on Granger causality, according to preprocessing of similar studies in this feld. A comparison for adults and children groups with a chi-square test was followed, employing the results of the Granger causality measures.

Results Results show that differences were mainly established for nodes inside the brain network connectivity. Additionally, for interactions between brain and heart networks, it was brought to light that children physiology sends more information from heart to brain nodes compared to the adults group.

Discussion This study represents a frst sight to children sleep analysis, employing the Granger causality computation. It contributes to understand sleep in children employing measurements from physiological signals. Preliminary fndings suggest more interactions inside the brain network for children group compared to adults group.

Granger causality - Polysomnography - Brain-heart connectivity - Physiological networks - Heart Rate Variability - Electroencephalography


Publication History

Received: 21 June 2021

Accepted: 05 October 2021

Article published online:
01 December 2023

© 2023. Brazilian Sleep Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • REFERENCIAS

  • 1 Sateia MJ. International classification of sleep disorders. Chest. 2014;146(5):1387–94.
  • 2 Sadeh A. III. Sleep assessment methods. Monogr Soc Res Child Dev. 2015;80(1):33–48.
  • 3 Blunden S, Galland B. The complexities of defining optimal sleep: empirical and theoretical considerations with a special emphasis on children. Sleep Med Rev. 2014;18(5):371–8.
  • 4 Åkerstedt T, Hume K, Minors D, Waterhouse J. The meaning of good sleep: a longitudinal study of polysomnography and subjective sleep quality. J Sleep Res. 1994;3(3):152–8.
  • 5 Kopasz M, Loessl B, Hornyak M, Riemann D, Nissen C, Piosczyk H, et al. Sleep and memory in healthy children and adolescents--a critical review. Sleep Med Rev. 2010;14(3):167–77.
  • 6 Vriend J, Davidson F, Rusak B, Corkum P. Emotional and cognitive impact of sleep restriction in children. Sleep Med Clin. 2015;
  • 7 de Bruin EJ, van Run C, Staaks J, Meijer AM. Effects of sleep manipulation on cognitive functioning of adolescents: a systematic review. Sleep Med Rev. 2017;32:45.57.
  • 8 Posada-Quintero HF, Reljin N, Bolkhovsky J, Orjuela-Canon AD, Chon K. Brain Activity Correlates With Cognitive Performance Deterioration During Sleep Deprivation. Front Neurosci. 2019;13:1001.
  • 9 Rossa KR, Smith SS, Allan AC, Sullivan KA. The effects of sleep restriction on executive inhibitory control and affect in young adults. J Adolesc Heal. 2014;55(2):287.92.
  • 10 Short MA, Banks S. The functional impact of sleep deprivation, sleep restriction, and sleep fragmentation. In: Sleep deprivation and disease. Springer; 2014. p. 13.26.
  • 11 Short MA, Blunden S, Rigney G, Matricciani L, Coussens S, Reynolds CM, et al. Cognition and objectively measured sleep duration in children: a systematic review and meta-analysis. Sleep Heal. 2018;4(3):292.300.
  • 12 El-Sheikh M, Sadeh A. I. Sleep and development: introduction to the monograph. Monogr Soc Res Child Dev. 2015;80(1):1.14.
  • 13 Bartsch RP, Liu KKL, Bashan A, Ivanov PC. Network physiology: how organ systems dynamically interact. PLoS One. 2015;10(11):e0142143.
  • 14 Ivanov PC, Bartsch RP. Network Physiology: Mapping Interactions Between Networks of Physiologic Networks. In: D’Agostino G, Scala A, editors. Networks of Networks: The Last Frontier of Complexity. Cham: Springer International Publishing; 2014. p. 203.22.
  • 15 Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep Med Rev. 2012;16(1):47.66.
  • 16 Bashan A, Bartsch RP, Kantelhardt JW, Havlin S, Ivanov PC. Network physiology reveals relations between network topology and physiological function. Nat Commun. 2012;3:702.
  • 17 Faes L, Nollo G, Jurysta F, Marinazzo D. Information dynamics of brain.heart physiological networks during sleep. New J Phys. 2014 Oct;16(10):105005.
  • 18 Faes L, Marinazzo D, Jurysta F, Nollo G. Granger causality analysis of sleep brain-heart interactions. In: Cardiovascular Oscillations (ESGCO), 2014 8th Conference of the European Study Group on. 2014. p. 5.6.
  • 19 Faes L, Marinazzo D, Jurysta F, Nollo G. Linear and non-linear brain--heart and brain--brain interactions during sleep. Physiol Meas. 2015;36(4):683.
  • 20 Guo S, Ladroue C, Feng J. Granger causality: theory and applications. In: Frontiers in Computational and Systems Biology. Springer; 2010. p. 83.111.
  • 21 Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, et al. American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Version 2.4. Darien. IL: American Academy of Sleep Medicine; 2017. p. 665.6.
  • 22 Faes L, Marinazzo D, Stramaglia S, Jurysta F, Porta A, Giandomenico N. Predictability decomposition detects the impairment of brain--heart dynamical networks during sleep disorders and their recovery with treatment. Phil Trans R Soc A. 2016;374(2067):20150177.
  • 23 Dumont M, Jurysta F, Lanquart J-P, Migeotte P-F, Van De Borne P, Linkowski P. Interdependency between heart rate variability and sleep EEG: linear/non-linear? Clin Neurophysiol. 2004;115(9):2031.40.
  • 24 Orjuela-Canon AD, Cerquera A, Freund JA, Julia-Serda G, Ravelo-Garcia AG. Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality. Comput Methods Programs Biomed. 2020;187.
  • 25 Jurysta F, Van De Borne P, Migeotte P-F, Dumont M, Lanquart J-P, Degaute J-P, et al. A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men. Clin Neurophysiol. 2003;114(11):2146.55.
  • 26 Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;(3):230.6.
  • 27 Camm AJ, Malik M, Bigger J, Breithardt G, Cerutti S, Cohen R, et al. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996;93(5):1043.65.
  • 28 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;289.300.
  • 29 Barnett L, Seth AK. The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. J Neurosci Methods. 2014;223:50.68.
  • 30 Schwab K, Skupin H, Eiselt M, Walther M, Voss A, Witte H. Coordination of the EEG and the heart rate of preterm neonates during quiet sleep. Neurosci Lett. 2009;465(3):252.6.
  • 31 Roffwarg HP, Muzio JN, Dement WC. Ontogenetic development of the human sleep-dream cycle. Science (80- ). 1966;
  • 32 Feinberg I, Campbell IG. Sleep EEG changes during adolescence: an index of a fundamental brain reorganization. Brain Cogn. 2010;72(1):56.65.
  • 33 Liu KKL, Bartsch RP, Lin A, Mantegna RN, Ivanov PC. Plasticity of brain wave network interactions and evolution across physiologic states. Front Neural Circuits. 2015;9:62.
  • 34 Jurysta F, Kempenaers C, Lanquart J-P, Noseda A, Van De Borne P, Linkowski P. Long-term CPAP treatment partially improves the link between cardiac vagal influence and delta sleep. BMC Pulm Med. 2013;13(1):29.
  • 35 Jurysta F, Lanquart J-P, Van De Borne P, Migeotte P-F, Dumont M, Degaute J-P, et al. The link between cardiac autonomic activity and sleep delta power is altered in men with sleep apnea-hypopnea syndrome. Am J Physiol Integr Comp Physiol. 2006;291(4):R1165--R1171.
  • 36 Jurysta F, Van De Borne P, Lanquart J-P, Migeotte P-F, Degaute J-P, Dumont M, et al. Progressive aging does not alter the interaction between autonomic cardiac activity and delta EEG power. Clin Neurophysiol. 2005;116(4):871.7.
  • 37 El-Sheikh M, Hinnant JB, Erath SA. VI. Marital conflict, vagal regulation, and children’s sleep: A longitudinal investigation. Monogr Soc Res Child Dev. 2015;80(1):89.106.
  • 38 El-Sheikh M, Kelly RJ, Philbrook LE. Sleep and development: Familial and socio-cultural considerations. In: Family contexts of sleep and health across the life course. Springer; 2017. p. 25.49.
  • 39 Silvani A, Calandra-Buonaura G, Dampney RAL, Cortelli P. Brain--heart interactions: physiology and clinical implications. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2067):20150181.
  • 40 Benarroch EE. Control of the cardiovascular and respiratory systems during sleep. Auton Neurosci Basic Clin. 2019;218:54.63.
  • 41 Yiallourou SR, Sands SA, Walker AM, Horne RSC. Maturation of heart rate and blood pressure variability during sleep in termborn infants. Sleep. 20δ12;35(2):177.86.