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17ABBAZC - Algorithms for Biosignal Processing in the C Language

Code Completion Credits Range Language
17ABBAZC KZ 2 1+1
Lecturer:
Tutor:
Supervisor:
Department of Information and Communication Technology in Medicine
Synopsis:

Algorithms for preprocessing and intelligent segmentation of the biological time-series in C and C++. Algorithms of FFT, SFFT and Wavelet Transform. Calculation of the cross-correlation and autocorrelation functions. Method of moving window, extraction of attributes. Example implementations of the fuzzy rules and neural network. Algorithms for design and realisation of the FIR a IIR filters. Methods of biosignal visualisation.

Requirements:

Solved and documented individual computer exercise.

Syllabus of lectures:

1. Review of selected data structures and properties of C language. Overview of available software digital signal processing libraries.

2. Introduction to C++, platform independent programs.

3. MS Windows platform - development tools and environments

4. GNU/Linux with X/Window System.- development tools and environments

5. Pocket PC platform - development tools and environments

6. Digitalization - practical problems concerning sampling and quantization. Algorithms for the computation of basic statistical parameters of time-series.

7. Time-domain. Moving window method.

8. Frequency domain - FFT algorithm, introduction to Wavelet transform

9. Time-Frequency domain - Short Time Fourier Transform, Spectrogram, Compressed Spectral Arrays

10. Algorithms for the computation of the Cross Correlation and Autocorrelation function. Convolution.

11. Algorithms for the FIR filters design.

12. Algorithms for the IIR filters design.

13. Principles of the methods for biosignal segmentation - extraction of the features from the time amd frequency domain, „measuring“ of the quality of extracted features. Introduction to the rule-systems and expert systems.

14. Implementation of the fuzzy rule-system. Trends and research centers in the area of the biological signal-processing. Ergonomy of the graphical user interface from the medical point of view.

Syllabus of tutorials:

1. Principles of the design of application interfaces of the libraries for biomedical data processing. Examples of available libraries.

2. Example of the implementation of the real-time biosignal acquisition algorithms on MS Windows platform.

3. Example of the implementation of the real-time biosignal acquisition algorithms on GNU/Linux with X/Window system.

4. Example of the implementation of the real-time biosignal acquisition algorithms on PocketPC 2003 (Windows CE) platform.

5. Implementation of selected statistical and time-domain analysis algorithms.

6. Implementation of selected frequency domain analysis algorithms (FFT).

7. Implementation of selected time-frequency domain analysis algorithms (CSA, CWT).

8. Implementation of the basic algorithms for the linear system identification.

9. Practice of the FIR and IIR filters design and implementation.

10. Realization of the algorithm for the fuzzy rule-system. Realization of the algorithm for the multilayer perceptron neural network, back-propagation algorithm.

11. Selection of individual exercises.

12. Solving of the individual exercise on the computer.

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:

Practical implementation of modern algorithms for the biosignal processing in C and C++. Leavers will be familiar with selected practical solutions of common algorithmic problems during biosignal processing: segmentation, analysis in time and frequency domain, design and implementation of FIR and IIR filters and methods of biosignal visualisation.

Study materials:

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

[1] William H. Press et al.: Numerical Recipes in C (7nd edition),. Cambridge University Press 2002

[2] Smith et al.: Digital Signal Processing (2nd edition), Kalifornia Technical Publishing, San Diego 2004

Note:
The course is a part of the following study plans:
Downloads:

Lectures: 
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PDF icon Presentations245.9 KB

Exercises: 
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PDF icon Tutorials245.25 KB