Computer-aided interpretation of fetal heart rate and uterine contraction has not been developed well enough for wide use. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography recordings, and timely prediction of fetal state during monitoring.

Cardiotocography used for fetal monitoring;new features developed using clinical expertise and system control theory characterise fetal heart rate response to contractions.

New machine learning models to predict Intrauterine Growth Restriction developed;Random Forest model had highest accuracy,but Support Vector Machine had better specificity.

Monitoring expectant mothers' health is important.Computerized fetal heart rate analysis helps assess heart rate variability.

Cardiotocography monitors fetal health. Machine learning models classify CTG in stages; SVM and RF had high accuracy.

Pathological fetal heart rate during labor associated with increased risk of neonatal acidosis.Repeated fetal monitoring and team training may reduce incidence of neonatal acidosis.

The study analyzes fetal heart rate categorization using decision trees with highest 98.7% accuracy and proves their viability for prediction according to classification .

The study provides doctors fetal heart rate indicators to reduce misdiagnosis. The algorithm analyzed 552 FHR signals and extracted 21 features. A model classifies FHR into categories based on features.

The electronic fetal heart monitor detects hypoxia; an algorithm analyzed heart data from 132 fetuses, identifying 22 parameters indicating abnormalities with 93.75% accuracy.

Intrapartum electronic fetal monitoring is used to monitor the fetus. Early uses led to more C-sections but issues were with interpretation, not the technology itself. Computerized FHR analysis systems were developed to address this.