Formation of functional associations across time scales in the fetal autonomic control system — A multifractal analysis

Gieraltowski, Jan; Hoyer, Dirk; Schneider, Uwe; Żebrowski, Jan J.

doi:10.1016/j.autneu.2015.03.007

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Results on multiscale entropy and multiscale multifractal analysis indicating dependencies of fetal heart rate variability (HRV) on gestational age (GA) were published earlier (Hoyer et al 2013a, Gieraltowski et al 2015). Here, we present new results on fetal heart rate variability development with GA using recurrence plot (RP) analysis.…”

Section: Recurrence Plot Analysis Of Fetal Heart Rate Variability—depmentioning

confidence: 99%

Monitoring fetal maturation—objectives, techniques and indices of autonomic function

et al. 2017

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Objective Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of ‘fetal programming’, also known as ‘developmental origins of adult disease hypothesis’, e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. Approach The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is key to fetal autonomic assessment. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Main Results Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. Significance The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

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Section: Recurrence Plot Analysis Of Fetal Heart Rate Variability—depmentioning

confidence: 99%

Monitoring fetal maturation—objectives, techniques and indices of autonomic function

et al. 2017

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“… 3 Moreover, maturation of the autonomic nervous system is a key controller in fetal cardiovascular regulation, 4 with reports showing a positive association between FHR variability and gestational age. 5 As IUGR is a pathologic condition in which fetuses do not grow as expected, based on their gestational age, being able to screen for this issue using fetal cardiotocography would be useful.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

et al. 2022

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Purpose Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal. Materials and Methods In total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome. Results In a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 ( p =0.031); Apgar score 1 min: odds ratio, 9.523 ( p <0.001), Apgar score 5 min: odds ratio, 11.49 ( p =0.001), and fetal distress: odds ratio, 23.09 ( p <0.001)]. Conclusion The machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.

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Self-Similarity and Detrended Fluctuation Analysis of Cardiovascular Signals

Castiglioni

Rienzo

Faini

2017

Complexity and Nonlinearity in Cardiovascular Signals

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Formation of functional associations across time scales in the fetal autonomic control system — A multifractal analysis

Cited by 5 publications

References 35 publications

Monitoring fetal maturation—objectives, techniques and indices of autonomic function

Monitoring fetal maturation—objectives, techniques and indices of autonomic function

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Self-Similarity and Detrended Fluctuation Analysis of Cardiovascular Signals

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