Prenatal diagnosis – indices of fetal developmenta.. (PRENATAL DIAGNOSIS)
Prenatal diagnosis – indices of fetal developmental disturbances enabled by advanced signal identification techniques
Start date: May 15, 2009,
End date: May 14, 2011
"Fetal programming of mental and cardiovascular diseases by adverse prenatal environmental influences is a main issue of developmental medicine. However, the number of measurable functional parameters in the human fetus is limited. The assessment of key parameters of the underlying processes of fetal brain maturation could foster the early detection of developmental problems, the design of therapeutic strategies and save immense cost for public health. Biomagnetic measurements of fetal heart rate patterns (HRP) and fetal auditory evoked responses (fAER) may open a new non-invasive diagnostic window. Up to now the signal to noise ratio of these recordings is non-sufficient and they are too short for an appropriate consideration of fetal state behaviour. Consequently reliable clinical studies reflecting developmental disturbances could not be performed. The goal of the project is two-fold: (1) to improve the identification of fetal heart beats and fAER in longer and non-stationary recordings, (2) to explore for the first time simultaneously behavioural state dependent HRP, indices of fetal heart rate variability and reliable fAER indices; both appropriate to data of a longitudinal study involving intrauterine growth restriction (IUGR) and antenatal steroid (glucocorticoid GC) administration. This will be achieved by innovative developments of advanced signal separation and identification methods to improve the signal to noise ratio in the biomagnetic recordings and novel signal analysis approaches best suited for nonlinear and nonstationary signals. The host group provides a unique data bank of functional, clinical and biomagnetic measurements from normal fetuses and those with possibly developmental disturbances by IUGR or GC. The developed methods will be evaluated on the study data set mentioned above and will be prerequisite for a significant improvement of the signal quality, reduction of dropouts and the design of advanced indices of fetal brain development."
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