NInFEA Citation (id = "citation-example-research-doi")

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      <text value="as reported on PhysioNet"/> 
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    <text value="Pani, D., Sulas, E., Urru, M., Sameni, R., Raffo, L., &amp; 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."/> 
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    <text value="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/con
    tent/ninfea/1.0.0/."/> 
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      <text value="NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology
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      <text value="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 value="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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        <value value="Danilo Pani, Eleonora Sulas, Monica Urru, Reza Sameni, Luigi Raffo, Roberto Tumbarello"/> 
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        <value 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."/> 
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