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A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to b...

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Autores principales: Sulas, Eleonora, Urru, Monica, Tumbarello, Roberto, Raffo, Luigi, Sameni, Reza, Pani, Danilo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838287/
https://www.ncbi.nlm.nih.gov/pubmed/33500414
http://dx.doi.org/10.1038/s41597-021-00811-3
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author Sulas, Eleonora
Urru, Monica
Tumbarello, Roberto
Raffo, Luigi
Sameni, Reza
Pani, Danilo
author_facet Sulas, Eleonora
Urru, Monica
Tumbarello, Roberto
Raffo, Luigi
Sameni, Reza
Pani, Danilo
author_sort Sulas, Eleonora
collection PubMed
description Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21(st) and 27(th) week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.
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spelling pubmed-78382872021-01-29 A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research Sulas, Eleonora Urru, Monica Tumbarello, Roberto Raffo, Luigi Sameni, Reza Pani, Danilo Sci Data Data Descriptor Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21(st) and 27(th) week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided. Nature Publishing Group UK 2021-01-26 /pmc/articles/PMC7838287/ /pubmed/33500414 http://dx.doi.org/10.1038/s41597-021-00811-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Sulas, Eleonora
Urru, Monica
Tumbarello, Roberto
Raffo, Luigi
Sameni, Reza
Pani, Danilo
A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title_full A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title_fullStr A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title_full_unstemmed A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title_short A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research
title_sort non-invasive multimodal foetal ecg–doppler dataset for antenatal cardiology research
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838287/
https://www.ncbi.nlm.nih.gov/pubmed/33500414
http://dx.doi.org/10.1038/s41597-021-00811-3
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