Schedule: 10.09.2019 - 09.12.2019
Teaching Period (valid 01.08.2018-31.07.2020):
Learning Outcomes (valid 01.08.2018-31.07.2020):
After this course the student can
1. recognise the main properties of a biosignal processing problem and assess what are appropriate techniques to use
2. explain the underlying principles of these signal processing techniques
3. design components of a full ‘signal processing and interpretation’ chain for a real-life biomedical problem
4. select and use methods to assess the usefulness/performance of this ‘processing chain’
5. understand and discuss the clinical / healthcare requirements for biosignal processing applications.
Content (valid 01.08.2018-31.07.2020):
Modern signal processing techniques in biomedical applications are discussed. Subjects include different spectral analysis methods, adaptive filters, classification methods, and time-frequency methods.
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Teaching methods: Lectures, exercises that can be done at home and are discussed afterwards in the classroom, a home exercise.
Requirements, Assessment Methods and Criteria: Passing the exam. Exercise scores contribute to grading.
Workload (valid 01.08.2018-31.07.2020):
- Lectures: 24 h
- Preparation to lectures: 24 h
- Exercise sessions: 12 h
- Independent solving of assignments: 30 h
- Independent studying of theory: 15 h
- 25 h
- Exam: 3 h
Study Material (valid 01.08.2018-31.07.2020):
Handouts of lecture slides. Additional material may be made available during the course.
Substitutes for Courses (valid 01.08.2018-31.07.2020):
Replaces courses Tfy-99.275 Signal Processing in Biomedical Engineering and Tfy-99.4275 Signal Processing in Biomedical Engineering.
Prerequisites (valid 01.08.2018-31.07.2020):
Basic knowledge of signal processing, e.g. course T-61.3015 or ELEC-C5230 or similar are compulsory. Basic skills in Matlab are required.
Grading Scale (valid 01.08.2018-31.07.2020):
0 to 5
Registration for Courses (valid 01.08.2018-31.07.2020):
Registration via WebOodi
- Teacher: Ivan Radevici