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Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography
Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the com...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009887/ https://www.ncbi.nlm.nih.gov/pubmed/36923461 http://dx.doi.org/10.3389/fbioe.2023.1059119 |
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author | Baldazzi, Giulia Sulas, Eleonora Vullings, Rik Urru, Monica Tumbarello, Roberto Raffo, Luigi Pani, Danilo |
author_facet | Baldazzi, Giulia Sulas, Eleonora Vullings, Rik Urru, Monica Tumbarello, Roberto Raffo, Luigi Pani, Danilo |
author_sort | Baldazzi, Giulia |
collection | PubMed |
description | Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden. |
format | Online Article Text |
id | pubmed-10009887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100098872023-03-14 Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography Baldazzi, Giulia Sulas, Eleonora Vullings, Rik Urru, Monica Tumbarello, Roberto Raffo, Luigi Pani, Danilo Front Bioeng Biotechnol Bioengineering and Biotechnology Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden. Frontiers Media S.A. 2023-02-27 /pmc/articles/PMC10009887/ /pubmed/36923461 http://dx.doi.org/10.3389/fbioe.2023.1059119 Text en Copyright © 2023 Baldazzi, Sulas, Vullings, Urru, Tumbarello, Raffo and Pani. 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 | Bioengineering and Biotechnology Baldazzi, Giulia Sulas, Eleonora Vullings, Rik Urru, Monica Tumbarello, Roberto Raffo, Luigi Pani, Danilo Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title | Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title_full | Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title_fullStr | Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title_full_unstemmed | Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title_short | Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
title_sort | automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009887/ https://www.ncbi.nlm.nih.gov/pubmed/36923461 http://dx.doi.org/10.3389/fbioe.2023.1059119 |
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