FOSTER: A Comprehensive Pipeline for Transabdominal Fetal Pulse Oximetry Validated in a Large Animal Model of Pregnancy.

Abstract

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. 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. 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. 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. 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.