General description

Course organized by TUT, see TUT study guide for up-to-date information.

Learning outcomes

After this course, the student can:
- describe the common properties of biosignals, and describe basic challenges in processing and analyzing them.
- explain the principles of filtering and spectral analysis and select suitable methods for applications in health and biology.
- analyze common methods of statistical modeling of biological data and explain the assumptions of the models.
- assess the performance of a developed biosignal processing or analysis method.
- apply signal processing methods to biological signals including EEG, ECG, gene expression.
- implement such methods to process biological signals.

Contents

- Types and origins of physiological and biological signals, and their basic properties. Basics of data acquisition, sampling, and filtering related to biosignals.
- Filtering of biosignals. Linear filtering, filter design for biosignals.
- Spectral analysis and its applications in biosignals.
- Statistical modelling of biological data. Classification problem.
- Performance estimation, hypothesis testing.
- Computer exercises with Matlab: applying signal processing and analysis methods in real biosignals (EEG, ECG, gene expression data).

Evaluation

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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