Biomedical Signal Analysis


At the end of this course, students should:

- Identify the typical characteristics of biosignals.

- Understand the principles on which some of the most advanced biosignals processing techniques are based.

- Compare the performance of the aforementioned tools.

- Be able to apply these tools to biosignals, and to extract from it clinical relevant information.

General characterization





Responsible teacher

Carla Maria Quintão Pereira , Ricardo Nuno Pereira Verga e Afonso Vigário


Weekly - 2

Total - 28

Teaching language



Students should have basic knowledge of signal processing and the origin of electrophysiological signals.


- Textbook of medical physiology (1996) A.C. Guyton, J.E. Hall; Saunders Company.
- Medical Physics and Biomedical Engineering (1999) B.H Brown, et al; Institute of Physics Publishing.
- Practical Biomedical Signal Analysis Using MATLAB (2012) K.J. Blinowska, J. Zygierewicz; CRC Press.
- Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals (2007) van Drongelen, W.; Academic Press.
- Detection and estimation methods for biomedial signals (1996) Metin Akay; Academic Press, San Diego.
- Advances in Cardiac Signal Processing (2007) Rajendra Acharva U, Jasiit S Suri, Jos AE Spaan SM Krishnan; Springer, New York.

Teaching method

The theoretical lectures will be delivered by the lecturer, using the support materials that are deemed necessary for each topic. These will include the black board, slides, applets, and demonstrations.
It is expected that students work autonomously both bibliographic research and in integration of the information therein.
As practice training, the students will carry out a laboratory project related to one particular subject covered during the course.

Evaluation method

2 tests (rated 0-20, rounded to the first decimal, minimum 9,5). Calculator is not allowed.

1 project (rated 0-20, rounded to the unit, minimum 10).

Permission to attend the final exam: succeed on the project, be present at, at least, 2/3 of laboratory lectures.

Final grade:

n1, n2 and n3 are the grades of the first three tests; and n4 is the grade of the project.

The arithmetic mean of the tests must be greater than or equal to 9.50 values.

Final grade: same as intermediary grade, if a passing grade was attained therein.

Final grade: 70% of a final exam + 30% of the project (needs to have passed it in the current year)

The exam grade must be greater than or equal to 9.50.

Subject matter

1. Introduction.
1.1 Types of biosignals
1.2 Non-stationarity of the biosignals
1.3 Intra and inter-variability
2. Advanced biosignals processing techniques
2.1 Wavelet transform
2.2 Principal component analysis
2.3 Independent component analysis
2.4 Dynamic signal analysis
2.5 Source Localization


Programs where the course is taught: