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DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network
BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937790/ https://www.ncbi.nlm.nih.gov/pubmed/31888592 http://dx.doi.org/10.1186/s12911-019-1007-5 |
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author | Zhao, Zhidong Deng, Yanjun Zhang, Yang Zhang, Yefei Zhang, Xiaohong Shao, Lihuan |
author_facet | Zhao, Zhidong Deng, Yanjun Zhang, Yang Zhang, Yefei Zhang, Xiaohong Shao, Lihuan |
author_sort | Zhao, Zhidong |
collection | PubMed |
description | BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. METHODS: In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. RESULTS: Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively CONCLUSIONS: Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately. |
format | Online Article Text |
id | pubmed-6937790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69377902019-12-31 DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network Zhao, Zhidong Deng, Yanjun Zhang, Yang Zhang, Yefei Zhang, Xiaohong Shao, Lihuan BMC Med Inform Decis Mak Research Article BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. METHODS: In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. RESULTS: Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively CONCLUSIONS: Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately. BioMed Central 2019-12-30 /pmc/articles/PMC6937790/ /pubmed/31888592 http://dx.doi.org/10.1186/s12911-019-1007-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Zhao, Zhidong Deng, Yanjun Zhang, Yang Zhang, Yefei Zhang, Xiaohong Shao, Lihuan DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title | DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title_full | DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title_fullStr | DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title_full_unstemmed | DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title_short | DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network |
title_sort | deepfhr: intelligent prediction of fetal acidemia using fetal heart rate signals based on convolutional neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937790/ https://www.ncbi.nlm.nih.gov/pubmed/31888592 http://dx.doi.org/10.1186/s12911-019-1007-5 |
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