Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 975-979, 2021 11.
Article
em En
| MEDLINE
| ID: mdl-34891451
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estetoscópios
Tipo de estudo:
Diagnostic_studies
Limite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article