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

• Type of Material: Article
• Authors: Eleonora Sulas, University of Cagliari; Monica Urru, Brotzu Hospital; Roberto Tumbarello, Brotzu Hospital; Luigi Raffo, University of Cagliari; Reza Sameni, Emory University; Danilo Pani, University of Cagliari
• Language: English
• Date: 2021-01-26
• Publisher: NATURE RESEARCH
• Publication Version: Final Published Version
• Copyright Statement: © 2021, The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41597-021-00811-3
• Title of Journal or Parent Work: SCIENTIFIC DATA
• Volume: 8
• Issue: 1
• Start Page: 30
• End Page: 30
• Keywords: Science & Technology; Science & Technology - Other Topics; Multidisciplinary Sciences
• Research Categories: Education, Technology; Physics, Radiation

Abstract

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 21st and 27th 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.