Syllabus academic year 2007/2008

Higher education credits: 6. Grading scale: TH. Level: G2 (First level). Language of instruction: The course might be given in English. Optional for: C4, C4sst, D4, D4sst, E4mt, F4, F4mt, Pi4bm. Course coordinator: Professor Leif Sörnmo,, Elektrovetenskap. Prerequisites: ESS040 Systems and Signals, ETI265 Signal Processing in Multimedia or ETT080 Signals and Communications. Assessment: Exam at the end of the course. Two mandatory laboratory exercises. Home page:

The course provides an overview of methods suitable for solving problems in biomedical signal processing. The student should obtain sufficient insights on the origin on biomedical signals and analysis methods to independently determine suitable methods.

Knowledge and understanding
For a passing grade the student must

• have knowledge about biomedical signals and methods which are particularly useful for their processing.

• to apply the most common methods on clinical problems (Matlab level)

• to define simple mathematical models and to determine related, optimal methods for estimation of relevant information.

Skills and abilities
For a passing grade the student must

• to understand the origin of bioelectrical signals and their manifestation on the body surface.

• to describe the most common clinical applications where such signals are used.

• to describe the most common methods for analysis of both periodical and aperiodical biomedical signals. The description is to be done in catchall terms, i.e., block diagrams and text, as well as with the help of equations.

• to formulate and describe statistical signal models being suitable for modelling of specific signal properties.

• to implement a method and evaluate its performance in clinically relevant terms.

Judgement and approach
For a passing grade the student must

• be able to analyze, assess, and implement algorithms, and also to interpret and to describe their inherent principles.

• have insight on the fact that seemingly different technical problems can be dealt with using the same methods.

Bioelectrical signals

• Their origin, especially concerning signals reflecting the activity of the brain, the muscles, and the heart.

• Information-carrying components in bioelectrical signals.

• Common clinical applications of bioelectrical signals.

Brain signals

• Analysis of both spontaneous activity and evoked potentials

• Spectral analysis (nonparametric and parametric) and characterization of power spectra.

• Time-frequency analysis.

L. Sörnmo, P. Laguna: Biomedical Signal Processing in Cardiac and Neurological Applications, Elsevier, ISBN 0-12-437552-9, 2005.