The course is for students with medical background. The course starts with an introduction to biomedical signals (or images). Furthermore the following topics are covered: basic concepts from time- frequency domain representation; noise cancellation; detection of events and objects; characterisation of shape- and complexity for waveforms and objects; frequency domain characterisation; machine learning and decision support.
Course description for study year 2023-2024. Please note that changes may occur.
Theoretical: Introduction to biomedical signals (and/or images); basic concepts from time- frequency domain representation; noise cancellation; detection of events and objects; characterisation of shape- and complexity for waveforms and objects; fequency domain characterisation; machine learning and decision support.
Laboratory activities: Introduction to data analysis tools relevant to the theoretical part of the course.
Knowledge: The subject aims to provide students with clinical background insight into concepts and skills important for handling problems in biomedical data analysis. Furthermore, insight into important applications of data analysis with examples from signal processing, image processing and machine learning. The candidate's project will decide whether the subject will emphasize signal or image processing. The subject shall provide competencies enabling the candidate to understand and apply research methodology used by researchers with a technological background. This will enable a more efficient collaboration between clinicians and technologists and contribute to translatory research.
Skills: The student will also be able to handle basic data analysis tools like MATLAB or Python to handle the type of problems described above. An introduction will give the basics in programming with use of control structures. The completion of the laboratory exercises in the course will depend on the student having acquires adequate programming skills.
General competence: At the completion of the course the student will be able to recognize problems which can be handled by data analysis methods. Furthermore, the student will be able to use the subject terminology of the course to define a problem precisely. The solution to the problem implies extraction of relevant information (e.g. for diagnosis) from a biomedical signal (or image) and use this information for decision support. This can be a diagnostic or therapeutic decision.The student also has to be able to handle various techniques for noise reduction and characterization of events and/or states in the biomedical signal (or objects in images).
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.