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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 01.10.2019 at 8:00 h and ends 25.10.2019 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, 18.10.2019 at 23:59 h. All later cancellations of registration will be considered as having failed the seminar.

  Time:   Preliminary meeting, DSS Library, 28.10.2019 at 16:30 h
Written report due on 17.02.2020
Final presentations, Aquarium, 26.02.2020 at 10:00 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.




Topic title   Description  
  Voice Tremor

A tremor can occur as a symptom of various diseases and can be studied to distinguish them. In addition to body parts such as the hands, arms or the head, whose tremors can be measured with an acceleration sensor, a voice tremor can also occur. This seminar investigates how different diseases can be classified using voice tremors. First, the features of different voice tremors should be discussed. Subsequently, classification algorithms used in the literature to differentiate between different diseases should be investigated.

  Simulation of Motor Unit
and Nerve Potentials
and Typical Model Parameters

Electroneuography (ENG) is the current gold standard of nerve assessment to investigate different signals of the nerve in the clinical environment. The electrical signal of peripheral nerves is quite low. Peak amplitudes in the mV range can be observed by using an external electric stimulation of the nerve. The electrical signal produced by the muscles also lies in the mV range. Different simulations are already published with different model parameters to access the individual signals of nerve fibers. This helps to simulate different human nerve diseases. Available simulations and also typical simulation parameters should be investigated from a signal analysis point of view in this seminar.

  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.

  Non-linear Drift Removal
to Estimate
Gait Kinematics

Inertial measurement units (IMUs) are combinations of 3D accelerometers and 3D gyroscopes that nowadays are often used for unconstrained human movement analysis. The naïve method to obtain position data and/or joint angles from the sensors is to integrate measured accelerations and angular velocities. However, IMUs suffer from time-varying and temperature-dependent offset, therefore drift arises in the final results of (double) integration. A typical drift removal technique is to subtract a linear fit from the integrated data. In this seminar, you will research about different drift removal techniques with an emphasis on non-linear methods.