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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Information Analysis of Biological Systems & Signals

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Code Completion Credits Range Language
17VIAB Z 2 1P+1C Czech
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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:

"Meloun a kol.: Kompendium statistického zpracování dat, ACADEMIA Praha, 2001

"Doukhan et al.: Tudory and Applications of Long-Range dependence, Birkhauser 2002

"Mackay D.: Information Theory. Cambridge University Press 2000

"Sprott: Chaos and Time-Series Analysis. Oxford University Press 2003

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:

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 exercise, 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:

All stud. materials (incl. syllabus, practical tasks etc.) are available on e-learning server <a href="http://skolicka.fbmi.cvut.cz">http://skolicka.fbmi.cvut.cz</a>

[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:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-03-28
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