Biomedical Signal & Image Processing & Modeling (Compulsory – 8 ECTS)

In this course, the fundamentals of digital signal processing with particular emphasis on problems in biomedical research, are explored. Lectures cover background topics on the biological signals and images as long as algorithmic approaches on biomedical signal analysis. Topics include biomedical signals basic principles, signals acquisition, filtering, feature extraction, and modelling. During the labs, practical examples in biomedical signal analysis are conducted using MATLAB, with focus on cardiology, neurology and motion analysis.

1. Fundamentals of Signal Processing

  • a. Data Acquisition: Sampling in time, aliasing, interpolation, and quantization.
  • b. Digital Filtering: Difference equations, FIR and IIR filters, basic properties of discrete-time systems, convolution.
  • c. DTFT: The discrete-time Fourier transform and its properties. FIR filter design using windows.

2. Biomedical Signals

  • a. Introduction, basic principles, signals aquation.
  • b. Electrocardiogram (ECG), cardiac electrophysiology, relation of electrocardiogram (ECG) components to cardiac events, basic ECG features, ECG analysis methodologies
  • c. Electroencephalogram (EEG), brain electrophysiology, basic EEG features, algorithms for EEG analysis

3. Image Segmentation and Registration

  • a. Image Segmentation: statistical classification, morphological operators, connected components.
  • b. Image Registration I: Rigid and non-rigid transformations, objective functions.
  • c. Image Registration II: Joint entropy, optimization methods.

4. Image Reconstruction

  • a. Mathematical Principles of Computed Tomography: Radon Transform; Fourier-Radon Theorem
  • b. Computed Tomography: X-rays; Scanners; Diagnostic Tools
  • c. MRI: Nuclear Magnetic Resonance; SHort Range Electromagnetic Fields; RF Electronics; Coils
  • d. Other Modalities: Ultrasound; PET; SPECT
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