CM2013 Signal Processing and Data Analytics in Biomedical Engineering 7.5 credits
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Content and learning outcomes
Course contents
The course is an introduction to the field of signal processing and data analytics in biomedical engineering. The focus is on providing a comprehensive introduction to the concepts of methods and techniques popular for the analysis of biosignals for medical, health, and sports applications. The course will at least cover the following topics:
•Origin and characteristics of biosignals and medical images
•Discretization of signals
oAnalogue-to-digital conversion processes
oSampling theorem and random sampling
•Applications and implementation of transform theory in biomedical applications
oSignal decomposition using Fourier series
oFourier Transform and Fast Fourier transform
oTime and Frequency Analysis
oOther relevant transforms methods
•Digital filters and their applications in biomedical engineering
oIntroduction to digital filters design
oApplication of filters and transform methods to 1-D (signals and time series) and 2-D signals (images)
•Stochastic processes and biosignal Modelling
oSpectrum estimation
•Types of noise and methods for noise reduction in biosignals
•Machine learning techniques and pattern recognition in biomedical applications
oFeature extraction and selection
oSupervised learning
oUnsupervised learning
•Methods and applications of multivariable data analysis in biomedical applications
•And other new developments in the field
Intended learning outcomes
After the course, students should be able to:
L1. Describe and discuss the principles of biomedical signal acquisition, sampling, and processing
L2. Characterize biosignals origin and noise features
L3. Design and implement fundamental biosignal analysis, modelling, and visualization tools
L4. Select and apply appropriate methods for pattern recognition and classification of biosignals for solving a given problem in biomedical engineering
L5. Design, motivate, implement, and evaluate a signal processing method for solving a specific problem in biomedical engineering
L6. Effectively work as part of a project team
Course disposition
Literature and preparations
Specific prerequisites
B.Sc. degree in engineering, social sciences or medical science (e.g. medical science or technology, engineering, applied physics, industrial management, entrepreneurship) or similar.
English 6.
Recommended prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- PRO1 - Project work, 2.0 credits, grading scale: P, F
- RED1 - Assignments, 2.0 credits, grading scale: P, F
- TEN1 - Written exam, 3.5 credits, grading scale: A, B, C, D, E, FX, F
The examiner may apply another examination format when re-examining individual students.
Final grade, grade scale A-F
The exam (A-F) determines the final grade for the course when all course parts have been passed. The examination form and grading criteria will be specified in a course-PM.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course web
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web CM2013