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HL2028 Biomedical Signal Processing 6.0 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus HL2028 (Autumn 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course is divided according to the different type of signals (ECG, EEG, etc). For each type of signal, methods for analyzing the signal are discussed. In computer laboratory experiments (mandatory) realistic clinical problems are illustrated.

Intended learning outcomes

When passed, the student should be able to:

  • Describe the origin, properties and suitable models of important biological signals such as ECG, EEG and EMG.
  • Determine and successfully apply suitable algorithms for analysis of biomedical signals. Specifically, the student should be able to implement and apply algorithms for
  • parametric and non-parametric estimation of a signal's power spectrum density.
  • filtering of signals

Literature and preparations

Specific prerequisites

Medical IT, Transform methods.

Recommended prerequisites

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• Sörnmo & Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Elsevier Academic Press, 2005 (eBook is available through KTH library)

• Digital Signal Processing: A Practical Approach (2nd Edition Emmanuel Ifeachor (Author), Barrie Jervis (Author) , 2001

• Hayes, Monson H. Statistical digital signal processing and modeling. Wiley. 2009. 

Course books may change for next term. More information will be sent to registered students.

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


  • LAB1 - Lab Work, 2.0 credits, grading scale: P, F
  • NÄR1 - Mandatory Attendance, 1.0 credits, grading scale: P, F
  • TEN1 - Examination, 3.0 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.

Other requirements for final grade

TEN1 – Written Exam, 3 hp, Grading scale A-F

LAB1 – Laboratory assignments, 2 hp

NÄR1 – Mandatory attendance, 1 hp           

To pass the course you have to:

   • Actively take part in 70% of mandatory lectures/exercises

   • If you miss more than 30% of lectures/exercises you should get at least 40% of written exam points to pass!

   • Be active in the field study, and prepare and hold a 30 minute seminar

   • Do individual assignments and give peer feedback to a fellow student

   • Do group project and give peer feedback to a fellow group work using the platform Bilda

Final grade

Point system

• Mandatory

    • Assignments, 10-20 points

    • Project,  35-45 points

    • Seminar, 5 Points

• Voluntary (If you have attend 70% of lectures)

    • Written exam, 0-30 points 

Maximum: 100 points

Minimum:  50 points (enough to pass! If you have attended 70% of lectures/exercises)


  0-50      F

  50-60    E

  60-70    D

  70-80    C

  80-90    B

  90-100  A      

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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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

Education cycle

Second cycle

Add-on studies

No information inserted


Farhad Abtahi