Computational and Mathematical Methods in Medicine

Pregnancy Monitoring


Publishing date
18 Oct 2013
Status
Published
Submission deadline
31 May 2013

Lead Editor

1Signal Processing Systems, Electrical Engineering Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands

2School of Science and Engineering, Reykjavik University, Menntavegur 1, 101 Reykjavík, Iceland

3Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy

4Applied Electronics and Information Engineering Department, University Politehnica of Bucharest, Iuliu Maniu 1-3, 060042 Bucharest, Romania

5UMR CNRS 7338 Biomécanique et Bioingénierie, Département de Génie Biologique, Université de Technologie de Compiègne, BP20529, Compiègne Cedex, France


Pregnancy Monitoring

Description

Pregnancy is a critical phase in the life of both a mother and her fetus, with important risks such as fetal distress and preterm delivery. Early assessment of these risks enables timely and effective intervention, which is essential to minimize perinatal mortality and long-term morbidity. Despite strong advances in sensing, patient monitoring, and image analysis techniques, their application in the area of pregnancy monitoring and diagnosis is still hampered by challenges such as movement artifacts, low signal-to-noise ratios, and complex signal interpretation.

There is therefore a strong need for new reliable methods to process and interpret biosignals and images that are measured noninvasively during pregnancy, aimed at enabling timely recognition and assessment of critical risks during pregnancy. This special issue focuses on such methods. Potential topics include, but are not limited to:

  • Analysis of fetal and uterine biopotentials
  • Ultrasound fetal imaging and Doppler analysis
  • Robust signal analysis and feature extraction
  • Analysis methods for distributed and/or multimodal sensing
  • Computational methods for pathophysiological modeling
  • Methods for feature selection and signal classification
  • Clinical studies for validation and feasibility evaluation of the proposed methods

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