ID 2429: SMARTbeat – Smart processing of non-invasive fetal ECG data from wearable sensors for an automatic detection of congenital heart diseases

Background:

Congenital heart defects are the most common organ malformations in newborns. The prevalence in Germany is around 1.1%. The chances of recovery are often good, but early diagnosis and treatment is necessary to avoid life-threatening complications in the case of critical heart defects. Despite the good standard of antenatal care in Germany, only around 12% of congenital heart defects are detected prenatally. The detection rate depends heavily on the type of malformation. 82% of diagnoses are made within the first three months of life. In addition to the fetal echocardiography, the non-invasive fetal electrocardiogram could also be a promising means of diagnosing congenital heart defects. This technology has not yet been researched extensively for prenatal use. However, there is initial evidence in the literature showing that it is possible to detect heart defects from the non-invasive fetal electrocardiogram. The goal of this thesis is to explore fetal ECG signals of healthy fetuses and fetuses with congenital heart defect. Data from the SMARTbeat study, which was collected at the Department of Gynecology and Obstetrics of the University Hospital in Erlangen, is being analyzed for this purpose. This dataset contains fetal ECG data from 44 pregnant women, including healthy fetuses and fetuses with congenital heart defects.

Tasks:

• Literature review on fetal electrocardiography and congenital heart defects
• Processing of fetal electrocardiography signals of healthy fetuses and fetuses with congenital heart defects
• Write thesis

Supervisors

Katharina Jäger, M. Sc.

Researcher & PhD Candidate

Dr.-Ing. Heike Leutheuser