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Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures
Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417534/ https://www.ncbi.nlm.nih.gov/pubmed/34490419 http://dx.doi.org/10.3389/frai.2021.674238 |
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author | Vargas-Calixto, Johann Warrick, Philip Kearney, Robert |
author_facet | Vargas-Calixto, Johann Warrick, Philip Kearney, Robert |
author_sort | Vargas-Calixto, Johann |
collection | PubMed |
description | Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability. |
format | Online Article Text |
id | pubmed-8417534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84175342021-09-05 Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures Vargas-Calixto, Johann Warrick, Philip Kearney, Robert Front Artif Intell Artificial Intelligence Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability. Frontiers Media S.A. 2021-08-20 /pmc/articles/PMC8417534/ /pubmed/34490419 http://dx.doi.org/10.3389/frai.2021.674238 Text en Copyright © 2021 Vargas-Calixto, Warrick and Kearney. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Vargas-Calixto, Johann Warrick, Philip Kearney, Robert Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title | Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title_full | Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title_fullStr | Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title_full_unstemmed | Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title_short | Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures |
title_sort | estimation and discriminability of doppler ultrasound fetal heart rate variability measures |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417534/ https://www.ncbi.nlm.nih.gov/pubmed/34490419 http://dx.doi.org/10.3389/frai.2021.674238 |
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