Advanced Predictive Analytics for Fetal Heart Rate Variability Using Digital Twin Integration.

Area of Science:

• Perinatal medicine
• Biomedical engineering
• Computational physiology

Background:

• Fetal heart rate variability (FHRV) is crucial for assessing fetal well-being and autonomic nervous system development.
• Conventional monitoring methods have limitations, potentially causing delayed interventions and suboptimal outcomes.
• Advanced predictive analytics are needed to enhance real-time fetal monitoring.

Purpose of the Study:

• To develop and validate an advanced predictive analytics approach for real-time fetal monitoring.
• To integrate approximate entropy analysis and a hidden Markov model (HMM) within a digital twin framework.
• To improve the prediction of fetal acid-base status and facilitate timely clinical interventions.

Main Methods:

• Utilized a dataset of 469 fetal electrocardiogram (ECG) recordings (>1 hour each).
• Preprocessed FHRV data and calculated approximate entropy to quantify complexity.
• Developed a hidden Markov model (HMM) with states representing pH levels and observed states from FHRV data, using Baum-Welch and Viterbi algorithms.

Main Results:

• Approximate entropy of FHRV showed significant correlations with umbilical cord blood gas parameters, especially pH.
• The HMM achieved 85% training and 79% testing accuracy on a balanced dataset, outperforming results on an imbalanced dataset (78% training, 71% testing).
• The model demonstrated effective capture of temporal dependencies for early prediction of fetal acid-base status.

Conclusions:

• The proposed digital twin framework with integrated predictive analytics enhances real-time fetal monitoring.
• This approach offers significant benefits for timely clinical interventions, potentially improving prenatal outcomes.
• The study highlights the utility of advanced computational methods in perinatal care.