CM2013 Signal Processing and Data Analytics in Biomedical Engineering 7.5 credits

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

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

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.

If the course is discontinued, students may request to be examined during the following two academic years.

• 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

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

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.

Mikael Forsman

• 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.