Abstract
Intrapartum fetal well-being assessment relies on fetal heart rate (FHR) monitoring. Studies have shown that FHR monitoring has a high false-positive rate for detecting fetal hypoxia during labor and delivery. Our group built a transabdominal fetal pulse oximeter (TFO) device that measures fetal oxygen saturation non-invasively through NIR light source and photodetectors in order to increase the accuracy of hypoxia detection. As light travels through both maternal and fetal tissue, photodetectors on the surface of mother’s abdomen capture mixed signals comprising fetal and maternal information. The fetal information should be extracted first to enable fetal oxygen saturation calculation. We investigate different methods to extract fetal information from mixed PPG signal and compute SpO2. One method is to employ signal processing techniques such as noise cancellation and peak detection and another method is to use a convolutional neural network. Once the fetal information is extracted, we can compute the fetal oxygen saturation. We have designed an algorithm that reads the received optical signal from the TFO and reports an estimate of fetal oxygen saturation (fetal SpO2) with a high correlation to actual measured fetal arterial blood oxygen saturation obtained from gold standard large animal models. The estimated fetal SpO2 with good calibration can report a wide range of oxygen levels, contrary to the conventional SpO2 monitoring devices calibrated for higher oxygen levels only.
Multiple evaluation methods are used to design and test the TFO system. Monte-Carlo simulation of photon propagation in tissue and circuit simulations are performed to optimize the design of TFO system. Benchtop tests are performed to quantify the signal-to-noise ratio of the fabricated TFO system. Animal models and Human Subjects are used to collect data and evaluate the performance of fetal SpO2 estimation algorithms.