Non-invasive fetal electrocardiogram and Doppler in early pregnancy

Made with heart. Why and how our dream became reality
Published in Research Data
Non-invasive fetal electrocardiogram and Doppler in early pregnancy
Like

Sardinia, the Italian region where the dataset was collected and the largest part of this research carried out, records an incidence of congenital heart disease about double that of the rest of Italy (about 15‰of live births, 18‰ if we also consider intrauterine deaths). Every year about 800 fetuses are cardiologically evaluated at the Pediatric Cardiology and Congenital Heart Disease Unit of the Brotzu Hospital in Cagliari, and many have arrhythmias with different levels of severity. Early discovery of fetal heart problems could allow in-utero treatment (trans-placental drug delivery) and birth planning, eventually leading to obtaining better prenatal prevention.

When we started the research in the field of non-invasive fetal electrocardiography (fECG), about 15 years ago, there were no certified medical devices able to provide a clinically acceptable, non averaged, fECG. To date, some devices are starting to appear but most of them still present severe limitations for a cardiological assessment of the fetus, so the research focused on new signal processing methods is still a hot topic. Several anomalies observed on the fetuses could be easily detected from an ECG but, for the time being, they can be only deduced with ultrasound techniques that, in turn, require very expensive instrumentation and expert cardiologists. This limits the screening to risky pregnancy.

Dr. R. Tumbarello performing a fetal echocardiographic exam for the development of the NInFEA multimodal dataset at the Pediatric Cardiology and Congenital Heart Disease Unit of the Brotzu Hospital in Cagliari, Italy.

The idea of developing a dataset for non-invasive fetal electrocardiography (fECG) dated about ten years ago, in the context of our research in the field. The lack of publicly available datasets that can be used for the development of signal processing algorithms aimed at the extraction of the small and elusive fECG signal from the instrumental and maternal physiological interferences, with high flexibility in the number and position of the electrodes on the maternal body, is a dramatic problem. It imposes to research groups the independent development of a proprietary dataset of real signals, with consequent considerable, often insurmountable, costs and difficulties. During the years, we struggled to find an adequate measurement setup, with huge costs for the acquisition of biopotential recording devices, which often proved unable to provide good quality signals, and clinical partners with enough experience on antenatal cardiological assessment to collect the signals with additional information on the fetal heart activity in early pregnancy, when no invasive methods can be adopted. Without that, no ground truth is available to understand the effectiveness of the signal processing methods.

We did it, and here is the result of our work, with the data available for free to everyone, and we will enlarge the dataset in the next few years with more signals. Several people worked hard to achieve this goal, and we are sure their dream is now a reality. We are sure this will foster the development of non-invasive fECG processing algorithms, with exciting new studies carried out without the burden of data acquisition. Eventually, we hope this will yield progress in fetal cardiac physiology knowledge and the development of prenatal diagnostics. 

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Research Data
Research Communities > Community > Research Data

Related Collections

With collections, you can get published faster and increase your visibility.

Remote sensing data for changes in land use

This Collection comprises a series of articles presenting data on changes to land use in urban areas, farmland, forests, and natural environments, as determined using remote sensing techniques.

Publishing Model: Open Access

Deadline: Jan 31, 2024

Medical imaging data for digital diagnostics

This Collection presents a series of articles describing annotated datasets of medical images and video. All medical specialities are considered and data can be derived from study participants, tissue samples, electronic health records (EHRs) or other sources.

Publishing Model: Open Access

Deadline: Dec 20, 2023