Logo ČVUT
CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Algorithms for Biosignals Processing in the C Language

The course is not on the list Without time-table
Code Completion Credits Range Language
F7ABBAZC KZ 2 1P+1C English
Garant předmětu:
Lecturer:
Jan Broulím
Tutor:
Jan Broulím
Supervisor:
Department of Information and Communication Technology in Medicine
Synopsis:

The principle and implementation of the most used algorithms for biosignal processing and their specific functional (and time and memory efficient) implementation in C and C ++ will be explained in the form of a practically oriented interpretation and demonstration tasks. Graduates will be acquainted with specific solutions to basic algorithmic problems in biosignal processing: with segmentation, analysis in the time and frequency domain, with the design of linear digital filters (FIR and IIR) and with the visualization of results. Prerequisites and co-requisites: basic knowledge of systems and signal processing, basics of ISO C. Output knowledge, skills, abilities and competences: The student is familiar with algorithms for preprocessing and intelligent segmentation of biological time series in C and C ++, eg: FFT algorithm, SFFT and wavelet transforms, algorithm for calculating autocorrelation and cross-correlation functions, convolution, etc. Can implement in C language the floating time window method for feature extraction and basic algorithms for the design and implementation of digital FIR and IIR filters. Understands and can implement in C language the basic ways of visualization of biological data and the results of their processing.

Requirements:

Solved and documented individual computer exercises.

Syllabus of lectures:

1. Representation of 1D signals in C language, useful signal and noise,

2. Memory allocation for 1D and 2D data (signals and images), loading data into computer memory,

3. Algorithms for point estimation of mean, variance and other statistical features,

4. Non-stationary signals, algorithm of calculation of symptoms by floating time window method.

5. Algorithms for histogram calculation, probability distribution estimation, noise generation, central limit theorem.

Algorithms for calculation of autocorrelation and cross-correlation functions, convolution, example of convolution machine.

6. Basics of 1D and 2D graphics in ISO C for visualization of biosignals. Code modularity and creation of portable function libraries in ISO C.

7. Implementation of selected numerical methods in C (iterative solution of nonlinear equations, numerical integration and derivation, approximation of experimental data),

8. Implementation of DFT, FFT and WFFT, Wavelet transform - time-frequency domain, calculation of spectrogram in ISO C.

9. Significance and use of impulse, transient and frequency characteristics of a linear system.

10. Design and implementation of digital FIR filters in C language I .: fourier series method,

11. Design and implementation of digital FIR filters in C II language: frequency sampling method,

12. IIR filters - Introduction, design and implementation in C language.

13. Algorithms for HRV analysis - implementation of algorithms in ISO C for calculation of linear parameters

14. Algorithms for HRV analysis - implementation of algorithms in ISO C for calculation of nonlinear parameters.

Syllabus of tutorials:

1. Use and creation of function libraries in C and C ++, code modularity. Design of an application interface for libraries for biosignal processing. Examples of available libraries.

2. Practical demonstration of implementation of algorithms for biomedical data collection on MS Windows and GNU / Linux platforms, including graphical display of results, partial independent task.

3. Algorithms for analysis of biosignals in the time domain - practical examples of algorithms for calculating basic statistical and other parameters, partial independent task.

4. Algorithms for analysis of biosignals in the frequency domain - example of implementation of the algorithm of fast Fourier transform (FFT) and continuous wavelet transform, partial separate task.

7. Algorithms for the analysis of biosignals in the time-frequency domain - an example of the implementation of the Gabor transform and the method of compressed spectral scenes (CSA), a partial independent task.

8. Algorithms for calculation of characteristics of linear systems: correlation functions, spectral densities, impulse responses, convolution sums.

9. Linear digital filters FIR and IIR, practical examples of design algorithms. Real application of filters to biological signals, comparison of methods - partial independent task including synthesis and implementation of the filter.

10. Algorithm for implementation of fuzzy rule system. Algorithm for neural network implementation (multilayer perceptron). Neural network training - partial independent task.

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:

Principles and implementation of the most used algorithms for biosignal processing and their specific functional (and time and memory efficient) implementation in C and C ++ in the form of practically oriented interpretation and demonstration tasks. Graduates will be acquainted with specific solutions to basic algorithmic problems in biosignal processing: with segmentation, analysis in the time and frequency domain, with the design of linear digital filters (FIR and IIR) and with the visualization of results. After completing the course the student will be familiar with the algorithms of preprocessing and intelligent segmentation of biological time series in C and C ++, eg: FFT algorithm, SFFT and wavelet transform, algorithm for calculating autocorrelation and cross-correlation functions, convolution, etc. Can implement the method in C language floating time window for flag extraction and basic algorithms for design and implementation of digital FIR and IIR filters. Understands and can implement in C language the basic ways of visualization of biological data and the results of their processing.

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] Byrne: Signal processing : a mathematical approach, CRC Press, Taylor & Francis Group, 2015

[2] Thareja: Programming in C, Second Edition, Oxford University Press, 2015.

[3] Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, Third Edition, 2007

Note:
Further information:
Výukové materiály pro tento předmět jsou zveřejněny prostřednictvím e-learningového systému na adrese <a href="https://skolicka.fbmi.cvut.cz">https://skolicka.fbmi.cvut.cz</a>
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-03-27
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6175506.html