Abstract
OBJECTIVE: Transabdominal fetal pulse oximetry (TFO) has the potential to supplement present intrapartum fetal monitoring approaches, which cannot accurately detect fetuses at risk of birth asphyxia. However, non-invasive measurement of fetal oxygen saturation (fSpO$_{2}$) is challenging due to dominant maternal tissue signals. We present methods to overcome such challenges, enabling robust and continuous fSpO$_{2}$ measurement. METHODS: We introduce FOSTER (Fetal Oxygen SaTuration EstimatoR), a comprehensive pipeline that combines novel signal processing and machine learning techniques to process photoplethysmogram (PPG) signals and estimate continuous fSpO$_{2}$. Using controlled desaturation experiments in pregnant ewes, we evaluate FOSTER's performance with both mixed maternal-fetal signals and isolated fetal components. RESULTS: Processing isolated fetal signals improves estimation accuracy with respect to arterial blood oxygen saturation (SaO$_{2}$), showing improvements of 13.7% in mean absolute error (MAE) and 10.5% in Pearson correlation coefficient relative to using mixed TFO signals alone. A dual-branch neural network, processing mixed and isolated PPG signals simultaneously, achieves additional improvements of 6.3% in MAE and 4.1% in Pearson correlation compared to using isolated fetal signals alone. CONCLUSION: The FOSTER pipeline demonstrates significant improvements in continuous fSpO$_{2}$ estimation accuracy through advanced signal processing and a dual-branch architecture, establishing a foundation for reliable fSpO$_{2}$ monitoring. SIGNIFICANCE: This work represents an important innovative step toward accurate, continuous, and non-invasive monitoring of fetal oxygenation. The validated methods in animal studies establish a foundation for advancing the development of fetal monitoring systems, offering new possibilities for improved maternal-fetal care.