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   https://doi.org/10.1109/ICCPS48487.2020.00019  

   Fong, Daniel D.; Vali, Kourosh; Ghiasi, Soheil (April 2020, 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS))
2. Validation of a Novel Transabdominal Fetal Oximeter in a Hypoxic Fetal Lamb Model

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   Fong, Daniel D.; Yamashiro, Kaeli J.; Johnson, Michael Austin; Vali, Kourosh; Galganski, Laura A.; Pivetti, Christopher D.; Farmer, Diana L.; Hedriana, Herman L.; Ghiasi, Soheil (June 2020, Reproductive Sciences)
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4. Transcutaneous Discrimination of Fetal Heart Rate from Maternal Heart Rate: A Fetal Oximetry Proof-of-Concept

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5. Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals

   https://doi.org/10.1109/EMBC46164.2021.9629814  

   Chen, Yuhan; Wilkins, Michael D.; Barahona, Jeffrey; Rosenbaum, Alan J.; Daniele, Michael; Lobaton, Edgar (December 2021, International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal. 

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