Pregnancies that are threatened by complications such as asphyxia, are often preceded by slight adaptations in the fetal movement pattern, including reduced fetal movements. Often this is one of the first signs. To improve early detection of fetuses at risk, we will develop reliable and easy-to-use methods to detect fetal movement and its relevant parameters. Early identification allows timely intervention and may thus aid in the prevention of severe complications. The project combines two strengths: physiological insights from physical models with pattern recognition from data-driven models. To this end you will develop algorithms that can be exploited to quantify fetal movements. Availability of accurate detection and prediction fetal movements allows for effective and timely intervention and may thus contribute to improved perinatal outcome. The proposed approach of your research includes: 1) Identification and evaluation of available methods for fetal movement detection, and the physiological relation between specific movement patterns and clinical outcome. 2) Development of a new monitoring modality, capable of reliable and easy-to-use fetal movement in a clinical setting, with potential for transfer to home setting. 3) Performing a feasibility study to verify the new monitoring modality in a (pre)clinical setting. 4) Contributing to setting up a prospective study for testing the new monitoring modality and set-up a database of fetal movement patterns across different risk levels, from which risk stratification will be feasible.
You will contribute to the e/MTIC project “PISANO” in collaboration with Eindhoven University of Technology, Máxima Medical Center and Philips Research and will work in a multidisciplinary team on all partner locations.
Academic and Research Environment
Eindhoven University of Technology (TU/e) is one of Europe’s top technological universities, situated in the heart of one of Europe’s largest high-tech innovation ecosystems. Research at TU/e is characterized by a combination of academic excellence and a strong real-world impact. This impact is pursued via close collaboration with high-tech industries and clinical partners.
Research related to this position will be carried out at the Biomedical Diagnostics (BM/d) lab of the Signal Processing Systems (SPS) group, which is part of the Electrical Engineering department. The BM/d lab has a strong track record in electrophysiological signal processing, physiological modelling and quantitative analysis of biosignals, ranging from ultrasound and MRI to electrophysiology. For more information, see here.
The candidate will have the opportunity to work with various members of the SPS group and will be tightly collaborating with Philips and Máxima MC.
For more information about the project and any informal enquiries, please contact
dr. E. Peri (e.peri[at]tue.nl), dr. B. van der Hout (m.b.v.d.hout[at]tue.nl) or S. Asvadi (sima.asvadi[at]philips.com).
For information about terms of employment, click here or contact HR (hrservices.flux[at]tue.nl).
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Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in interviews.
Application deadline: 31/01/2022
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