Skip to main content
Till KTH:s startsida

CM2013 Signal Processing and Data Analytics in Biomedical Engineering 7.5 credits

In this course, the students will learn about methods and techniques for data acquisition, preprocessing and noise reduction, pattern recognition and feature extraction and basic machine learning and classification methods to specific biomedical applications based on required specifications and constraints. The course is divided into theory lectures, computer exercises and lab and project work.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus CM2013 (Spring 2022–)
Headings with content from the Course syllabus CM2013 (Spring 2022–) are denoted with an asterisk ( )

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 

  • Analogue-to-digital conversion processes
  • Sampling theorem and random sampling 

Applications and implementation of transform theory in biomedical applications

  • Signal decomposition using Fourier series
  • Fourier Transform and Fast Fourier transform
  • Time and Frequency Analysis 
  • Other relevant transforms methods

Digital filters and their applications in biomedical engineering

  • Introduction to digital filters design
  • Application of filters and transform methods to 1-D (signals and time series) and 2-D signals (images)

Stochastic processes and biosignal Modelling

  • Spectrum estimation

Types of noise and methods for noise reduction in biosignals

Machine learning techniques and pattern recognition in biomedical applications

  • Feature extraction and selection
  • Supervised learning
  • Unsupervised 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

Literature and preparations

Specific prerequisites

Completed a 15 credit thesis in engineering, social sciences, medicine, biomedical engineering, applied physics, industrial economics, or entrepreneurship. Alternatively, one year of work experience in medical technology, technical physics, computer science, electrical engineering, or entrepreneurship. English B/6.

Recommended prerequisites

To successfully follow the course, the following prerequisites are recommended:

  • Basic knowledge in signal processing.
  • Familiarity with programming in Matlab or Python.

Equipment

No information inserted

Literature

No information inserted

Examination and completion

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

Grading scale

A, B, C, D, E, FX, F

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

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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Medical Engineering, Technology and Health

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

Farhad Abtahi (sabt@kth.se)