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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...

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Autores principales: Xiao, Yahui, Lu, Yaosheng, Liu, Mujun, Zeng, Rongdan, Bai, Jieyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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