MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Medical Diagnostics & Devices (TSM_MedDD)

This module gives an introduction to the physical and technical principles and applications of common 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.

Prerequisites

Engineering mathematics (algebra, calculus, numerical methods), physics, electricity of BSc engineering programs or similar (including systems and signals, Fourier transform)

Learning Objectives

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 (XR, CT, MRI, Ultrasound)
  • achieve basic knowledge of the most important clinical application of medical imaging modalities
  • describe approaches and methods for image reconstruction and quality assessment

Contents of Module

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

Principles of Ultrasound and MRI

Generation of X-ray, X-ray detectors, technology and application of Fluoroscopy, CT

Image quality, radiation protection and QA for diagnostic devices

Part 2 Signal processing

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.)

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

Teaching and Learning Methods

Presentations, Excersises and Labs

 

Literature

Dance

DR, Christofides S, Maidment ADA, McLean ID, Ng

KH (Eds): Diagnostic

Radiology

Physics.

Vienna, 2014: IAEA, ISBN 978-92-0-131010-1

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.

Download full module description

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