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A deep feature fusion network for fetal state assessment
CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which ca...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748093/ https://www.ncbi.nlm.nih.gov/pubmed/36531165 http://dx.doi.org/10.3389/fphys.2022.969052 |
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author | Xiao, Yahui Lu, Yaosheng Liu, Mujun Zeng, Rongdan Bai, Jieyun |
author_facet | Xiao, Yahui Lu, Yaosheng Liu, Mujun Zeng, Rongdan Bai, Jieyun |
author_sort | Xiao, Yahui |
collection | PubMed |
description | CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features’ diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal’s temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method’s effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works. |
format | Online Article Text |
id | pubmed-9748093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97480932022-12-15 A deep feature fusion network for fetal state assessment Xiao, Yahui Lu, Yaosheng Liu, Mujun Zeng, Rongdan Bai, Jieyun Front Physiol Physiology CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features’ diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal’s temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method’s effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works. Frontiers Media S.A. 2022-11-30 /pmc/articles/PMC9748093/ /pubmed/36531165 http://dx.doi.org/10.3389/fphys.2022.969052 Text en Copyright © 2022 Xiao, Lu, Liu, Zeng and Bai. 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 | Physiology Xiao, Yahui Lu, Yaosheng Liu, Mujun Zeng, Rongdan Bai, Jieyun A deep feature fusion network for fetal state assessment |
title | A deep feature fusion network for fetal state assessment |
title_full | A deep feature fusion network for fetal state assessment |
title_fullStr | A deep feature fusion network for fetal state assessment |
title_full_unstemmed | A deep feature fusion network for fetal state assessment |
title_short | A deep feature fusion network for fetal state assessment |
title_sort | deep feature fusion network for fetal state assessment |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748093/ https://www.ncbi.nlm.nih.gov/pubmed/36531165 http://dx.doi.org/10.3389/fphys.2022.969052 |
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