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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

haracterisation of neonatal cardiac dynamics using ordinal partition networ

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Author(s):
Santos, Laurita dos [1] ; Correa, Debora C. [2, 3] ; Walker, David M. [2] ; de Godoy, Moacir F. [4] ; Macau, Elbert E. N. [5] ; Small, Michael [2]
Total Authors: 6
Affiliation:
[1] Univ Brasil, Inst Sci & Technol, BR-08230030 Sao Paulo, SP - Brazil
[2] Univ Western Australia, Dept Math & Stat, Complex Syst Grp, Crawley, WA 6009 - Australia
[3] Univ Western Australia, ARC Ind Transformat Training Ctr Transforming Mai, Crawley, WA 6009 - Australia
[4] Fac Med Sao Jos Rio Preto, Dept Cardiol & Cardiovasc Surg, BR-15090000 Sao Jose Dos Campos, SP - Brazil
[5] Univ Fed Sao Paulo, Inst Sci & Technol, BR-12247014 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 5
Document type: Journal article
Source: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 60, n. 3, p. 829-842, MAR 2022.
Web of Science Citations: 0
Abstract

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. (AU)

FAPESP's process: 18/03517-8 - Association between heart rate variability and salivary cortisol for the evaluation of individuals under stress conditions by means of nonlinear analysis and artificial intelligence methods
Grantee:Laurita dos Santos
Support Opportunities: Regular Research Grants
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants