MSE Master of Science in Engineering

The Swiss engineering master's degree

Chaque module vaut 3 ECTS. Vous sélectionnez 10 modules/30 ECTS parmi les catégories suivantes:

  • 12-15 crédits ECTS en Modules technico-scientifiques (TSM)
    Les modules TSM vous transmettent une compétence technique spécifique à votre orientation et complètent les modules de spécialisation décentralisés.
  • 9-12 crédits ECTS en Bases théoriques élargies (FTP)
    Les modules FTP traitent de bases théoriques telles que les mathématiques élevées, la physique, la théorie de l’information, la chimie, etc., vous permettant d’étendre votre profondeur scientifique abstraite et de contribuer à créer le lien important entre l’abstraction et l’application dans le domaine de l’innovation.
  • 6-9 crédits ECTS en Modules contextuels (CM)
    Les modules CM vous transmettent des compétences supplémentaires dans des domaines tels que la gestion des technologies, la gestion d’entreprise, la communication, la gestion de projets, le droit des brevets et des contrats, etc.

Le descriptif de module (download pdf) contient le détail des langues pour chaque module selon les catégories suivantes:

  • leçons
  • documentation
  • examen 
Medical Diagnostics & Devices (TSM_MedDD)

This module gives an introduction to the physical and technical principles and applications of important diagnostic modalities. Starting with an overview of clinically used modalities and their applications, technical requirements and limitations based on the fundamental principles will be discussed. Furthermore, efficient methods for biomedical signal processing and analysis are introduced.

Compétences préalables

Basics in maths, physics, electricity of BSc engineering programs or similar

Objectifs d'apprentissage

Upon completition of the module, the student will be able to

  • gain knowledge in fundamentals of chemical, biological and physical sensors
  • achieve basic knowledge in the design of sensor systems
  • apply sensors and systems in medical diagnostics
  • apply signal processing methods on biosignals for diagnostic purposes
  • achieve basic signal processing skills to perform artifact removal, feature extraction, and classification on biological signals.
  • explain fundamental principle of medical imaging modalities
  • achieve basic knowledge of the most important clinical application of medical imaging modalities
  • describe approaches and methods for image quality assessment

Catégorie de module

Part 1 Devices & Sensors: Overview of diagnostic instrumentation and modalities:

Generation of X-ray, X-ray detectors, technology and application of Fluoroscopy, CT, PET / SPECTMRI, diagnostic ultra sound; image-guided therapy (interventional radiology, IGRT, theranostics);

Image quality, radiation protection and QA for diagnostic devices, 

Chemical, biological, and physical sensors, design requirements for sensors and devices in diagnostics, sensor application in medical diagnostics (ECG, EEG, EMG, optical pulsoxymetrie, (Blood)pressure, flow sensor, otoacoustic emission (OAE), etc.)

 
Part 2 Signal processing

Measurement in medical diagnostics, amplifier, signal conversion and quantization

Standard methods for biomedical signal processing and analysis, Imaging processing

  •  Background on time- and frequency-domain characteristics of particular biosignals and common artifacts
  •   Techniques for artefact removal, event detection, feature extraction, pattern recognition, classification

Méthodes d'enseignement et d'apprentissage

Presentations, Excersises and Labs

 

Bibliographie

Oppelt A (Ed.): Imaging Systems for Medical Diagnostics. Siemens, Publicis Corporate Publishing, Erlangen; ISBN 3-89578-226-2

John D. Enderle, Joseph D. Bronzino, Introduction to Biomedical Engineering, Academic Press

R. A. Wildhaber et al., "Signal detection and discrimination for medical devices using windowed state space filters," 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, 2017, pp. 125-133.

R. A. Wildhaber et al., "Windowed State-Space Filters for Signal Detection and Separation," in IEEE Transactions on Signal Processing, vol. 66, no. 14, pp. 3768-3783, 15 July15, 2018.

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