Generated Narrative: Citation

identifier: FEvIR Object Identifier: 60

title: NInFEA Citation

status: active

date: 2021-09-24T10:41:01.74Z

publisher: Computable Publishing LLC

contact: support@computablepublishing.com

description: A citation of a dataset

copyright: https://creativecommons.org/licenses/by-nc-sa/4.0/

summary

style: as reported on PhysioNet ()

text: Pani, D., Sulas, E., Urru, M., Sameni, R., Raffo, L., & Tumbarello, R. (2020). NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (version 1.0.0). PhysioNet. https://doi.org/10.13026/c4n5-3b04.

summary

style: Computable Publishing (citation-summary-style#comppub)

text: NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research [Dataset], version 1.0.0. Contributors: Danilo Pani, Eleonora Sulas, Monica Urru, Reza Sameni, Luigi Raffo, Roberto Tumbarello. In: PhysioNet, DOI 10.13026/c4n5-3b04. Published November 12, 2020. Accessed March 17, 2021. Available at: https://physionet.org/content/ninfea/1.0.0/.

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Titles

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*	primary-human-use ()	English (Tags for the Identification of Languages[4.0.1]#en)	NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research

Abstracts

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*	Primary human use (CitedArtifactAbstractType#primary-human-use)	English (Tags for the Identification of Languages#en)	The development of algorithms for the extraction of the foetal ECG (fECG) from non-invasive recordings is hampered by the lack of publicly-available reference datasets, which could be used to benchmark different algorithms while providing a ground truth on the foetal heart activity when an invasive scalp lead is unavailable. By enriching the electrophysiological recordings with simultaneous multimodal signals, these datasets could also help the investigation of the foetal cardiac physiology, providing ground truth for the analysis in early pregnancy, when the fECG is not directly accessible. The Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA) is the first open-access dataset featuring simultaneous non-invasive electrophysiological recordings, fetal pulsed-wave Doppler (PWD) and maternal respiration signals. The dataset includes 60 entries from 39 voluntary pregnant women, between the 21st and the 27th week of gestation. Every entry is composed of 27 electrophysiological channels (2048 Hz, 22 bits, acquired by means of the TMSi Porti7 system), maternal respiration signal (through a resistive thoracic belt), synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians at the time of the signal collection.

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citation: Sulas, E., Urru, M., Tumbarello, R., Raffo, L., Sameni, R., Pani, D., A non-invasive multimodal foetal ECG–Doppler dataset for antenatal cardiology research. Sci Data 8, 30 (2021). https://doi.org/10.1038/s41597-021-00811-3

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articleDate: 2020-11-12

language: English (Tags for the Identification of Languages[4.0.1]#en)

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url: https://physionet.org/static/published-projects/ninfea/ninfea-non-invasive-multimodal-foetal-ecg-doppler-dataset-for-antenatal-cardiology-research-1.0.0.zip

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contributorship

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value: Danilo Pani, Eleonora Sulas, Monica Urru, Reza Sameni, Luigi Raffo, Roberto Tumbarello

summary

type: acknowledgements ()

source: copied-from-article ()

value: The authors wish to thank the Pediatric Cardiology and Congenital Heart Disease Unit, Brotzu Hospital (Cagliari, Italy), where the dataset was collected, and all the voluntary pregnant women for their kindness in giving their signals for this research. The authors gratefully thank Alessandra Cadoni, Graziella Secchi, Luisa Aru, Elisa Farris, Chiara Fenu, Elisa Gusai, Giulia Baldazzi, Giulia Pili for their support in the recording of the signals included in this dataset. Part of this research was supported by the Italian Government—Progetti di InteresseNazionale (PRIN) under the grant agreement 2017RR5EW3 - ICT4MOMs project. Eleonora Sulas is grateful to Sardinia Regional Government for supporting her PhD scholarship (P.O.R.F.S.E., European Social Fund 2014-2020). Reza Sameni acknowledges the funding from the European Research Council Advanced Grant Number 320684, on Challenges in the Extraction and Separation of Sources (CHESS) for his contribution in this research, provided during his appointment at GIPSA-lab, Grenoble Alpes University, Grenoble, France.