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Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data
Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294944/ https://www.ncbi.nlm.nih.gov/pubmed/37370663 http://dx.doi.org/10.3390/bioengineering10060730 |
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author | Asfaw, Daniel Jordanov, Ivan Impey, Lawrence Namburete, Ana Lee, Raymond Georgieva, Antoniya |
author_facet | Asfaw, Daniel Jordanov, Ivan Impey, Lawrence Namburete, Ana Lee, Raymond Georgieva, Antoniya |
author_sort | Asfaw, Daniel |
collection | PubMed |
description | Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0–10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors. |
format | Online Article Text |
id | pubmed-10294944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102949442023-06-28 Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data Asfaw, Daniel Jordanov, Ivan Impey, Lawrence Namburete, Ana Lee, Raymond Georgieva, Antoniya Bioengineering (Basel) Article Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0–10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors. MDPI 2023-06-19 /pmc/articles/PMC10294944/ /pubmed/37370663 http://dx.doi.org/10.3390/bioengineering10060730 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Asfaw, Daniel Jordanov, Ivan Impey, Lawrence Namburete, Ana Lee, Raymond Georgieva, Antoniya Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title | Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title_full | Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title_fullStr | Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title_full_unstemmed | Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title_short | Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data |
title_sort | multimodal deep learning for predicting adverse birth outcomes based on early labour data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294944/ https://www.ncbi.nlm.nih.gov/pubmed/37370663 http://dx.doi.org/10.3390/bioengineering10060730 |
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