Fetal autonomic brain age scores, segmented heart rate variability analysis, and traditional short term variability

Abstract

Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R2 = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R2 = 0.648 in 30 min recordings, R2 = 0.610 in active sleep segments of 10 min, and R2 = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R2 = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures.

Gestational diabetes alters the fetal heart rate variability during an oral glucose tolerance test: a fetal magnetocardiography study

“…These observations are thought to reflect a gain of vagal influence, maturation of the fetal nervous system, and hence overall fetal development . Furthermore, fetal HRV as such depends on multiple influences including maternal blood pressure, thermoregulation and respiration as well as fetal behavioural, activation and sleeping states . However, no significant differences in HRV were observed for time of day and fetal gender …”

“…12 Hence, measurements of fetal HRV are a promising procedure to assess fetal well-being and, presumably, overall neurodevelopmental state. [13][14][15] In the present study, we hypothesised that the maternal metabolism (e.g. gestational diabetes) impacts on the fetal ANS being revealed under metabolic stress conditions like an OGTT.…”

“…It furthermore interacts with different brain areas, including brainstem, amygdala and cortical regions . Hence, measurements of fetal HRV are a promising procedure to assess fetal well‐being and, presumably, overall neurodevelopmental state …”

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

“…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.…”

Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis

“…Primarily because of the non-linear interplay of different physiological control loops in generating the heart rate, linear methods are not adequate to fully describe such a complex system. Several non-linear HRV methods such as fractal scaling analysis, higher order spectra analysis, multi-scale entropy analysis, power law analysis, complexity analysis, symbolic dynamics analysis and heart rate turbulence analysis have been studied for various diseases [7][8][9][10][11][12][13][14][15]. It must be taken into account that while the collection of heart rate (HR) was initially only possible with expensive laboratory-based electrocardiograph recorders, the recent availability of specifically designed portable recorders has substantially boosted the use of HRV monitoring [16,17].…”