Circuits and Systems
Dept. of Microelectronics

Biomedical signal processing/wavefield imaging

Contact: Rob Remis

  • Signal processing and imaging (estimation and detection for diagnostics and therapy), in particular Magnetic Resonance Imaging (MRI) from EM modeling and instrument design to signal processing, image formation (numerical inversion techniques) and tissue parameter estimation, with applications to medical diagnostics in collaborations with university hospitals.
  • Biomedical signals estimation and detection: Estimation of signal parameters from multichannel signals (e.g. ECG); detection and localization of medical conditions such as heart fibrillation, epilepsy.

Projects under this theme

Earlier recognition of cardiovascular diseases

Atrial Fibrillation FIngerPrinting: Spotting Bio-Electrical Markers to Early Recognize Atrial Fibrillation by the Use of a Bottom-Up Approach

Good Vibrations - Fast and Robust Wave Field Computations in Complex Structures Using Krylov Resonance Expansions

Using Krylov subspace reduction techniques to solve wave field problems in complex media (resonanting nano-scale devices and seismic exploration)

Dielectric enhanced MRI

Modeling and analyzing the effect of high permittivity pads in MRI imaging

Agenda

  • Thu, 23 Aug 2018
  • 14:00
  • LB 01.170 Timmanzaal

MSc SS Thesis Presentation

Guillermo Ortiz Jiménez

Multidomain Graph Signal Processing: Learning and Sampling

Sparse sampling for tensors and graphs

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  • Thu, 30 Aug 2018
  • 10:00
  • HB 01.230

MSc SS Thesis Presentation

Haidong Hao

Vessel Layer Separation of X-ray Angiographic Images using Deep Learning Methods

Fast solutions based on a fully convolutional network (FCN) trained by conventional loss or adversarial loss.

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  • Fri, 31 Aug 2018
  • 10:30

MSc TC Thesis Presentation

Feng Ma

Respiration monitoring based on information fusion from Impedance pneumography and Electrocardiography

With known respiration information, corresponding parameters in both time domain (respiratory rate) and frequency domain (respiratory power) can be extracted to indicate the health condition.

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MSc students