Electronic Fetal Monitoring–Prevention or Rescue?

Schifrin, Barry S.

doi:10.3389/fped.2020.00503

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is important to highlight that so far fHR monitoring has not been enough to prevent fetal injury during labor and, consequently, long-term complications. In fact, the role of fHR monitoring (electronic fetal monitoring, EFM, to be precise), despite being used in over 90% of hospitals during delivery, remains controversial (Schifrin, 2020 ). There is no clarity on its usefulness in decreasing perinatal mortality or cerebral palsy when measured by a CTG which has been explained by the incorrect focus of EFM on prediction of acidemia, a poor correlate to fetal injury, instead of predicting fetal cardiovascular decompensation per se as well as the limitations of CTG technologies in capturing the short-term time scale fluctuations of HRV reflecting vagal modulations (Alfirevic et al, 2017 ; Frasch et al, 2017 ; Frasch, 2018 ; Gold and Frasch, 2021 ).…”

Section: The Environmental Factors Affecting the Ans Developmentmentioning

confidence: 99%

A Review on the Vagus Nerve and Autonomic Nervous System During Fetal Development: Searching for Critical Windows

Cerritelli¹,

Frasch²,

Antonelli³

et al. 2021

Front. Neurosci.

View full text Add to dashboard Cite

The autonomic nervous system (ANS) is one of the main biological systems that regulates the body's physiology. Autonomic nervous system regulatory capacity begins before birth as the sympathetic and parasympathetic activity contributes significantly to the fetus' development. In particular, several studies have shown how vagus nerve is involved in many vital processes during fetal, perinatal, and postnatal life: from the regulation of inflammation through the anti-inflammatory cholinergic pathway, which may affect the functioning of each organ, to the production of hormones involved in bioenergetic metabolism. In addition, the vagus nerve has been recognized as the primary afferent pathway capable of transmitting information to the brain from every organ of the body. Therefore, this hypothesis paper aims to review the development of ANS during fetal and perinatal life, focusing particularly on the vagus nerve, to identify possible “critical windows” that could impact its maturation. These “critical windows” could help clinicians know when to monitor fetuses to effectively assess the developmental status of both ANS and specifically the vagus nerve. In addition, this paper will focus on which factors—i.e., fetal characteristics and behaviors, maternal lifestyle and pathologies, placental health and dysfunction, labor, incubator conditions, and drug exposure—may have an impact on the development of the vagus during the above-mentioned “critical window” and how. This analysis could help clinicians and stakeholders define precise guidelines for improving the management of fetuses and newborns, particularly to reduce the potential adverse environmental impacts on ANS development that may lead to persistent long-term consequences. Since the development of ANS and the vagus influence have been shown to be reflected in cardiac variability, this paper will rely in particular on studies using fetal heart rate variability (fHRV) to monitor the continued growth and health of both animal and human fetuses. In fact, fHRV is a non-invasive marker whose changes have been associated with ANS development, vagal modulation, systemic and neurological inflammatory reactions, and even fetal distress during labor.

show abstract

Section: The Environmental Factors Affecting the Ans Developmentmentioning

confidence: 99%

A Review on the Vagus Nerve and Autonomic Nervous System During Fetal Development: Searching for Critical Windows

Cerritelli¹,

Frasch²,

Antonelli³

et al. 2021

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The prevailing focus on fetal acidemia as the primary indicator of fetal distress has been challenged, suggesting the need for a more expansive understanding of fetal-maternal physiology and advocating for a less defensive posture towards monitoring. Additionally, the persistent concern of obstetrical malpractice, often centered around the interpretation of EFM tracings, introduces a further layer of complexity to this issue [4].…”

Section: A Introductionmentioning

confidence: 99%

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Alkurdi,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

In the realm of obstetrics, the evaluation of fetal health remains a paramount yet challenging endeavor. Traditional approaches, such as electronic fetal monitoring (EFM), despite their widespread adoption, continue to grapple with uncertainties regarding their impact on neonatal outcomes and the reduction of emergency cesarean deliveries. This ambiguity is compounded by a prevailing confusion within the obstetric community about interpreting fetal heart rate patterns, often leading to inconsistent and subjective assessments. Addressing these complexities, our study presents an innovative machine learning-based techniques for the comprehensive classification of fetal health using cardiotocogram (CTG) data, offering a more objective and nuanced alternative to conventional methods. The core of our proposed solution is a novel model employing a sophisticated ensemble of machine learning classifiers, including Multi-Support Vector Machine (Multi-SVM), Decision Tree, Random Forest with Hyperparameter Tuning, XGBoost, and Neural Networks. This model is unique in its application, processing datasets in four different forms: raw datasets, datasets processed with MinMaxScaler, datasets subjected to feature selection using SelectKBest, and a combination of MinMaxScaler processing and SelectKBest feature selection. Such meticulous preprocessing, encompassing normalization and feature selection, is pivotal in ensuring equitable contribution from each feature, thereby optimizing the model's learning process and predictive accuracy. The effectiveness of our model is rigorously evaluated using a dataset comprising 2126 individual records from CTG exams, classified by specialist obstetricians into three types: Normal, Suspect, and Pathological. These records are exhaustively analyzed using various metrics, including Accuracy, Precision, Recall, F1-Score, ROC AUC, and Confusion Matrix. Among the classifiers, XGBoost emerged as the most proficient, consistently outperforming others across multiple metrics. This indicates its superior ability to accurately identify and categorize the different states of fetal health. Our findings thus underscore the significant promise of machine learning in revolutionizing fetal health monitoring, offering a more reliable, objective, and comprehensive method for assessing fetal well-being, with profound implications for prenatal care and clinical decision-making.

show abstract

Pathophysiological interpretation of fetal heart rate tracings in clinical practice

Jia

Ghi

Pereira

et al. 2023

American Journal of Obstetrics and Gynecology

View full text Add to dashboard Cite

Electronic Fetal Monitoring–Prevention or Rescue?

Cited by 3 publications

References 19 publications

A Review on the Vagus Nerve and Autonomic Nervous System During Fetal Development: Searching for Critical Windows

A Review on the Vagus Nerve and Autonomic Nervous System During Fetal Development: Searching for Critical Windows

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Pathophysiological interpretation of fetal heart rate tracings in clinical practice

Contact Info

Product

Resources

About