Fetal Health Classification from Cardiotocograph for Both Stages of Labor-A Soft-Computing-Based Approach.

To date, cardiotocography (CTG) is the only non-invasive and cost-effective tool available for continuous monitoring of the fetal health. In spite of a marked growth in the automation of the CTG analysis, it still remains a challenging signal processing task. Complex and dynamic patterns of fetal heart are poorly interpreted.

A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set.

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression.

Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach.

Computerized techniques for Cardiotocograph (CTG) based labor stage classification would support obstetrician for advance CTG analysis and would improve their predictive power for fetal heart rate (FHR) monitoring. Intrapartum fetal monitoring is necessary as it can detect the event, which ultimately leads to hypoxic ischemic encephalopathy, cerebral palsy or even fetal demise. To bridge this gap, in this paper, we propose an automated decision support system that will help the obstetrician identify the status of the fetus during ante-partum and intra-partum period.