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Title: Contextually-aware Fetal Sensing in Transabdominal Fetal Pulse Oximetry
Authors:
; ;
Award ID(s):
1934568 1838939
Publication Date:
NSF-PAR ID:
10182978
Journal Name:
2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS)
Page Range or eLocation-ID:
119 to 128
Sponsoring Org:
National Science Foundation
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  1. 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 furthermore »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.« less