Information Analysis of Biological Systems & Signals

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Information and Communication Technology in Medicine

Synopsis:

Information entropy, applications. Average mutual information. Continuous and

discrete communication channel. Relationship of information and thermodynamic

entropy. Principle of maximal entropy. Biosystem organization, models and system

identification. Introduction of statistical decision making, testing of

statistical hypothesis, Bayessian approach.

Requirements:

Syllabus of lectures:

1. Introduction to the probabilistic theory and information theory. Properties

of the information entropy.

2. Average mutual information, methods of computation.

3. Continuous and discrete communication channel, capacity of the communication

channel.

4. Relation of the information and

ermodynamical entropy.

5. Maximum entropy principle. Organisation of the huge natural systems.

6. Deterministic and stochastic models of the biosystems.

7. Finite automaton - deterministic, fuzzy and stochastic approach.

8. Methods of system identification from the point of view of information

theory.

9. Introduction to statistical reasoning.

10. Basic parameric and non-parametric test of statistical hypotheses.

11. Bayesian approach.

12. Connection between invariant parameters of the biological time-series.

13. Coding and compression of the biomedical data.

14. Introduction to data-mining. Contemporary research centers and Information

sources.

Syllabus of tutorials:

Study Objective:

Information entropy, applications. Average mutual information. Continuous and

discrete communication channel. Relationship of information and thermodynamic

entropy. Principle of maximal entropy. Biosystem organization, models and system

identification. Introduction of statistical decision making, testing of

statistical hypothesis, Bayessian approach.

Study materials:

[1] Mackay D.: Information Theory. Cambridge University Press 2000

[2] William H. Press et al.: Numerical Recipes in C. Cambridge University Press

1992

[3] Kantz, Schreiber: Nonlinear Time Series Analysis, Cambridge University

Press, 2002

[4] Sprott: Chaos and Time-Series Analysis, Oxford University Press, 2003

[5] Mackay D.: Information Theory. Cambridge University Press 2000

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: