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

Author:

Sulas Eleonora,Urru Monica,Tumbarello Roberto,Raffo Luigi,Sameni Reza,Pani DaniloORCID

Abstract

AbstractNon-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.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis of various techniques for ECG signal in healthcare, past, present, and future;Biomedical Engineering Advances;2023-11

2. Open Data: Valuable Resources and Opportunities for the Researchers in Fetal Cardiac Monitoring;Innovative Technologies and Signal Processing in Perinatal Medicine;2023-08-19

3. Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography;Frontiers in Bioengineering and Biotechnology;2023-02-27

4. Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0;BMC Pregnancy and Childbirth;2023-01-16

5. Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG;2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI);2022-09-27

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