Marker Evaluation of quantitative Variables in Electroneurography

Marker Evaluation of characteristic quantitative Variables in Electroneurography

In this project – a group of three students – are analyzing how precise quantitative variables could be manually or automatically extracted in Electroneurography, which is an essential measurement method for the assessment of peripheral nerve disorders. For electrophysiological diagnostics, muscle signals are recorded with surface electrodes. The signals are triggered by electrical stimulation of efferent nerve fibers at different sites along the nerve. The recorded waveforms are analyzed by measuring quantities such as nerve conduction velocity, signal amplitude, distal motor latency. These are compared with age-matched reference values. These objective data are a relevant contribution to the diagnosis of nerve pathologies in addition to the clinical examination.

Compared to algorithms from digital signal processing, these measurements are either imprecise setting characteristic signal variables by hand in the recorded signals, or they result in non-uniform results if electroneurography equipment of different manufacturers is used for the measurement. Additionally, no standardized algorithms are currently available for the calculation of quantitative variables, which makes a comparison of clinical studies quite challenging and prohibits the usage of big data and artificial intelligence.

The project task is to develop a web-based survey, which enables medical persons to participate from their clinical computer and to set time- and amplitude markers by hand. This survey allows to collect statistics characteristic markers. Afterwards, the results of the different markers are compared in terms of accuracy to a standard electroneurography system and advanced digital signal processing algorithms. The motivation of this project is to enable the use of modern signal analysis methods in the neurologic application by showing the impact of optimal calculated quantitative variables in electroneurography. This could pave the way for machine learning for electrophysiological diagnostics in the near future.

Participating Students

  • Tobias Philipp
  • Lukas Fonk
  • Christian Kanarski

Supervisors

  • Eric Elzenheimer
  • Gerhard Schmidt
  • Kevin Prehn

Student Projects - Overview

The DSS team offers student projects. With these projects we try to support the idea of project-based learning. This means that most of our projects are designed such, that a multitude of studends can work on that project. Beside solving the individual task, studends should also learn how to split the problem into smaller subproblems and to organize the resulting subprojects. Software packages such as SVN, etc. should support this idea.

Past and ongoing student projects:

Usually, these projects are announced in the lecture "Signals and Systems". However, if you are interested in such projects and do not participate in this lecture, please contact Prof. Schmidt for further details.

In addition, we participate in support programs for women in science. If you are interested in doing a research-based project, please contact us. However, please be aware that such programms are not always active and usually support women in their master study period.

Past and ongoing woman support projects:

Contact

Prof. Dr.-Ing. Gerhard Schmidt

E-Mail: gus@tf.uni-kiel.de

Christian-Albrechts-Universität zu Kiel
Faculty of Engineering
Institute for Electrical Engineering and Information Engineering
Digital Signal Processing and System Theory

Kaiserstr. 2
24143 Kiel, Germany

How to find us

Recent News

TensorFlow and Escape Rooms

The last two days (Monday and Tuesday, September 16 and 17, 2019), the whole DSS team did a course on TensorFlow and Keras. Since we already have interfaces of TensorFlow to our real-time tool KiRAT, it was time now to extend our knowledge on graphs and the corresponding training of weights, biases, etc. After two days of hard work we booked two escape rooms and solved the "secrets" hidden in ...


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