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Seminar "Selected Topics in Medical Signal Processing"


Basic Information

Seminar overview  
  Lecturers:   Gerhard Schmidt and group  
  Semester:   Winter term  
  Language:   English or German  
  Target group:   Master students in electrical engineering and computer engineering  
  Prerequisites:   Fundamentals in digital signal processing  

If you want to sign up for this seminar, you need to register with the following information in the registration form

  • surname, first name,
  • e-mail address,
  • matriculation number,

Please note that the registration period starts 01.10.2020 at 8:00 h and ends 28.10.2020 at 23:59 h. All applications before and after this registration period will not be taken into account.

Registration will be possible within the before mentioned time under the following subsite - Seminar Registration.

During the registration process you will also choose your seminar topic. Only one student per topic is permitted (first come - first serve).

The registration is binding. A deregistration is only possible by sending an e-mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Sunday, 18.10.2020 at 23:59 h. All later cancellations of registration will be considered as having failed the seminar.

  Time:   Preliminary meeting, Zoom, 04.11.2020 at 15:00 h
Written report due on 26.02.2021
Final presentations, Zoom, 03.03.2021 at 08:00 h

Students write a scientific report on a topic closely related to the current research of the DSS group. Potential topics, therefore, deal with digital signal processing related to medical signal processing.

Students will also present their findings in front of the other participants and the DSS group.



Topics for WS 20/21

Topic title   Description  
  Collision Testing
of Nerves and their Conduction
Velocity Distributions

The electrical signal of peripheral human nerves is relatively low. Peak amplitudes in the μV range can be recorded directly form the nerve using an electric stimulation. Electroneurography is the current gold standard of nerve assessment to investigate the nerve conduction velocity of a human. Collision testing is an exciting technique to enhance standard diagnostics further, and it is currently not used in clinical routine. This testing method enables to describe a nerve's conduction characteristics in more detail and could improve neurophysiological diagnostics. In this seminar, collision testing and standard distribution patterns of conduction velocities should be investigated in particular from a medical signal processing perspective.

  Video Processing
for Tremor Analysis

A tremor can occur as a symptom of various diseases and can be studied to distinguish them. Acceleration and EMG sensors can be used to analyse the tremor of different parts of the body such as hands, arms or the head. In this seminar should be investigated how this typical analysis can be supported by video. For this purpose, different methods of video processing used in the literature will be examined. In addition, the advantages of different methods for tremor measurement should be discussed.

  Neural Networks for Noise
Reduction Signal Processing

Neural networks can be used for different applications, for instance classification of different diseases. Besides these typical approaches, neural networks can also be used for the reduction of noise in measured signals. In this seminar, an overview about attempts using neural networks for noise reduction should be considered by means of literature study. Finally, a comparison of the different attempts should be given.

  Sequence Models to Detect
Gait from Accelerometry

Advances in wearable sensor technology, e.g. inertial measurement units (IMUs), cause a shift from lab-based to home-based human gait analysis. Key to reliable home-based gait analysis is the accurate recognition of gait episodes, so-called walking bouts. In this seminar you will investigate which machine learning-based algorithms, preferably recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) are best suited for segmentation and classification of walking bouts from acceleration signals. You will provide an overview of algorithms and to which (public) datasets they have been applied. Eventually, you will outline what methods could be applied in a cohort of neuro-degenerative disease people.

  Human Motion Modelling
Using Kinematic Chains

Kinematic chains are a powerful control theory approach also used in the medical domain to mathematically describe human motion. In addition to universal models, there are kinematic chains targeted at specific parts of the human body during activities like running. In this seminar the student should identify and describe several of those specific approaches based on literature research. The approaches should be compared with special focus on the potential advantages and drawbacks of open vs. closed kinematic chains.