Information Analysis of Biological Systems and Signals

Garant předmětu:

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:

Solved and documented individual computer exercise.

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 termodynamical 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:

1. Practical examples of computation of the information and entropy from the biological time-series.

2. Algorithms for the computation of the average mutual information

3. Examples of the communication channel, estimation of the capacity.

4. Example of the deterministic and stochastic model of the biological systems.

5. Application of the parametric and non-parametric statistical test.

7. Experimental comparison of selected compression algorithms applied to biosignals.

8. Selection of individual exercises.

9. Analysis of individual execise, time-plan.

10.-13. Solving of the individual exercise on the computer.

14. Solving of the individual exercise on the computer.

Presentation and verification of the individual exercises.

Study Objective:

Extension of system aproach in biological time-series

analysis. Comprehension and relations of systems, signals,

information and entropy. Practical application of modern

methods of information analysis to digital processing of

biological data.

Study materials:

[1] Doukhan et al.: Tudory and Applications of Long-Range

dependence, Birkhauser 2002

[2] Mackay D.: Information Theory. Cambridge University

Press 2000

[3] Sprott: Chaos and Time-Series Analysis. Oxford

University Press 2003

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Biomedical Informatics - combined study (compulsory elective course)