Universal characteristics of evolution and development are inherent in fetal autonomic brain maturation

Schmidt, Alexander; Schukat-Talamazzini, Ernst Günter; Zöllkau, Janine; Pytlik, A; Leibl, S; Kumm, Kathrin; Bode, Franziska; Kynass, Isabelle; Witte, Otto W.; Schleußner, E; Schneider, Uwe; Hoyer, Dirk

doi:10.1016/j.autneu.2018.02.004

Cited by 12 publications

(10 citation statements)

References 52 publications

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“…The HRV indices used include the most common ones and were organized according to their signal processing origin into categories of amplitude, complexity and patterns ( Table 2). All HRV indices were calculated from preprocessed NN interval series after removal of artifacts and decelerations according to previous analyses (Hoyer et al, 2014;Schmidt et al, 2018a). Recordings were considered only if less than 5 % of their time length had been corrupted by artifacts.…”

Section: Hrv Indices and Categoriesmentioning

confidence: 99%

“…The myriad different HRV indices applied in individual studies hinders easy and reliable comparisons of methods and results. Some of the indices are redundant, and others discriminative only with respect to particular influences or disturbances (TaskForce, 1992;Stein, 2005;Maestri et al, 2007;Schmidt et al, 2018b). Within categories of HRV amplitude, complexity, and heart rate patterns, respectively, we expect certain redundancies between the category members.…”

Section: Introductionmentioning

confidence: 99%

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Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

et al. 2019

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Fetal development and its disturbances have been reported to be associated with a multiplicity of HRV indices. Furthermore, these HRV indices change with maturation. We propose the abstraction of HRV categories defined by short-and long-term fluctuation amplitude, complexity, and pattern indices that cover all relevant aspects of maturational age, behavioral influences and a series of pathological disturbances. The study data are provided by multiple centers. Our approach is an important step towards the goal of a standardized diagnostic tool for early identification of fetal developmental disturbances with respect to the reduction of serious complications in the later life.

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Section: Hrv Indices and Categoriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

et al. 2019

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“…An overview of the calculated parameters and the used transformation functions can be seen in table 1. For calculation details please see Schmidt et al [6].…”

Section: Methodsmentioning

confidence: 99%

Pattern-Segmented Heart Rate Variability Analysis During Fetal Maturation

Schmidt

Hoyer

Schneider

2018

2018 Computing in Cardiology Conference (CinC)

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Background: Established fetal maturation diagnostics evaluates heart rate patterns (HRP) such as baseline, variability, decelerations (DC) and accelerations (AC).Fetal heart rate variability (fHRV) parameters provide valuable additional information. Their dependence on those patterns is crucial but not sufficiently explored. Objective of the present work is the comparison of the maturational age prediction using fHRV parameters with respect to those patterns.Methods: We analyzed 555 recordings, each one lasting 30 minutes, of normal fetuses, from 19 weeks of gestation (WGA) onward. We applied a patternsegmented fHRV analysis of linear and nonlinear parameters under consideration of the fetal behavioral states to evaluate the WGA prediction accuracy, based on linear regression models that were tested using a repeated cross validation scheme.Results/Conclusion: fHRV parameters calculated under exclusion of DCs, show a significantly improved age dependency compared to the standard method, especially in the early weeks of the second half pregnancy. This aspect may improve the early sensitive identification of maturation disorders of the fetus.

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“…Using fMCG, Hoyer and colleagues defined the fetal autonomic brain age score (fABAS) ( Table 2 ), which derives from a multivariate analysis of five different metrics—amplitude, gMSE(3), skewness, pNN5, and VLF/LF—and managed to assess ANS development from the 22nd week GA ( Hoyer et al, 2013 , 2015 ). Other authors have then proposed to add other metrics to fABAS to define more complete and precise models ( Schmidt et al, 2018 ).…”

Section: The Ans Physiology Underlying Hrv and Its Use In The Neonatamentioning

confidence: 99%

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Chiera¹,

Cerritelli²,

Casini³

et al. 2020

Front. Neurosci.

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Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often timeconsuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a noninvasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/painrelated behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant's HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant's clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients' health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

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Universal characteristics of evolution and development are inherent in fetal autonomic brain maturation

Cited by 12 publications

References 52 publications

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

Pattern-Segmented Heart Rate Variability Analysis During Fetal Maturation

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

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