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Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many...

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Autores principales: Nieto-del-Amor, Félix, Beskhani, Raja, Ye-Lin, Yiyao, Garcia-Casado, Javier, Diaz-Martinez, Alba, Monfort-Ortiz, Rogelio, Diago-Almela, Vicente Jose, Hao, Dongmei, Prats-Boluda, Gema
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471282/
https://www.ncbi.nlm.nih.gov/pubmed/34577278
http://dx.doi.org/10.3390/s21186071
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author Nieto-del-Amor, Félix
Beskhani, Raja
Ye-Lin, Yiyao
Garcia-Casado, Javier
Diaz-Martinez, Alba
Monfort-Ortiz, Rogelio
Diago-Almela, Vicente Jose
Hao, Dongmei
Prats-Boluda, Gema
author_facet Nieto-del-Amor, Félix
Beskhani, Raja
Ye-Lin, Yiyao
Garcia-Casado, Javier
Diaz-Martinez, Alba
Monfort-Ortiz, Rogelio
Diago-Almela, Vicente Jose
Hao, Dongmei
Prats-Boluda, Gema
author_sort Nieto-del-Amor, Félix
collection PubMed
description One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
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spelling pubmed-84712822021-09-27 Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals Nieto-del-Amor, Félix Beskhani, Raja Ye-Lin, Yiyao Garcia-Casado, Javier Diaz-Martinez, Alba Monfort-Ortiz, Rogelio Diago-Almela, Vicente Jose Hao, Dongmei Prats-Boluda, Gema Sensors (Basel) Article One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice. MDPI 2021-09-10 /pmc/articles/PMC8471282/ /pubmed/34577278 http://dx.doi.org/10.3390/s21186071 Text en © 2021 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
Nieto-del-Amor, Félix
Beskhani, Raja
Ye-Lin, Yiyao
Garcia-Casado, Javier
Diaz-Martinez, Alba
Monfort-Ortiz, Rogelio
Diago-Almela, Vicente Jose
Hao, Dongmei
Prats-Boluda, Gema
Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title_full Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title_fullStr Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title_full_unstemmed Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title_short Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
title_sort assessment of dispersion and bubble entropy measures for enhancing preterm birth prediction based on electrohysterographic signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471282/
https://www.ncbi.nlm.nih.gov/pubmed/34577278
http://dx.doi.org/10.3390/s21186071
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