Noninvasive transabdominal fetal pulse oximetry can provide clinicians critical assessment of fetal health and potentially contribute to improved management of childbirth. Conventional pulse oximetry through continuous wave (CW) light has challenges measuring the signals from deep tissue and separating the weak fetal signal from the strong maternal signal. Here, we propose a new approach for transabdominal fetal pulse oximetry through interferometric near-infrared spectroscopy (iNIRS). This approach provides pathlengths of photons traversing the tissue, which facilitates the extraction of fetal signals by rejecting the very strong maternal signal from superficial layers. We use a multimode fiber combined with a mode-field converter at the detection arm to boost the signal of iNIRS. Together, we can detect signals from deep tissue (>∼1.6 cm in sheep abdomen and in human forearm) at merely 1.1 cm distance from the source. Using a pregnant sheep model, we experimentally measured and extracted the fetal heartbeat signals originating from deep tissue. This validated a key step towards transabdominal fetal pulse oximetry through iNIRS and set a foundation for further development of this method to measure the fetal oxygen saturation.
Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals
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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- Award ID(s):
- 1915599
- NSF-PAR ID:
- 10351531
- Date Published:
- Journal Name:
- International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Page Range / eLocation ID:
- 975 to 979
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EMBC46164.2021.9629814
