Course detail

Advanced Analysis of Biological Signals

FEKT-MPA-ACSAcad. year: 2026/2027

The course focuses on advanced methods of biosignal processing, particularly wavelet transformation, empirical mode decomposition, and related analytical techniques. Students will become familiar with sampling rate conversion, filtering, compression, and the analysis of biological signals. The course also covers the use of smartphones in telemedicine and the application of modern filtering methods, including linear deconvolution and nonlinear approaches. Předmět se zaměřuje na pokročilé metody zpracování biosignálů, zejména na vlnkovou transformaci, empirickou modální dekompozici a související analytické techniky. Studenti se seznámí s konverzí vzorkovacího kmitočtu, filtrací, kompresí a analýzou biologických signálů. Součástí kurzu je také využití chytrých telefonů v telemedicíně a aplikace moderních filtračních metod, včetně lineární dekonvoluce a nelineárních přístupů.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Lukáš Smital, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Offered to foreign students

The home faculty only

Entry knowledge

The student should have knowledge of digital signal processing. They should be familiar with different methods of describing linear systems (transfer function, impulse response, difference equation, frequency response). Basic knowledge of biosignal characteristics (especially ECG, EEG, EMG) is expected. In computer-based exercises, familiarity with the Matlab programming environment is assumed.

Rules for evaluation and completion of the course

Knowledge test (selected topics from lectures and laboratory exercises) - 10 points.
Individual project (software solution + report + presentation) - 20 points.
Final exam (written form) - 70 points.

To be admitted to the final exam, a minimum of 15 points must be obtained from the test and project, and a minimum of 35 points must be obtained from the written exam. Laboratory exercises are mandatory.

 

Znalostní test (vybraná témata z přednášek a laboratorních cvičení) - 10 bodů.
Individuální projekt (programové řešení + zpráva + prezentace) - 20 bodů.
Závěrečná zkouška (písemnou formou) - 70 bodů.

Pro připuštění k závěrečné zkoušce je nutné z testu a projektu získat minimálně 15 bodů, z písemné zkoušky je nutné získat minimálně 35 bodů. Laboratorní cvičení jsou povinná. 

Aims

The aim of the course is to provide students with a deeper understanding of advanced biosignal processing methods, focusing on wavelet transformation, empirical mode decomposition, and related analytical techniques. Students will become familiar with the theoretical principles and practical implementation of these methods. Emphasis is placed on applications in biomedical engineering and telemedicine, including the use of smartphones.

A graduate of the course will be able to:
- perform sampling rate conversion and explain its impact on signal processing
- design and implement filters with sampling rate conversion
- understand the principles of wavelet transformation and apply it for filtering, compression, and analysis of biological signals
- analyze data using empirical mode decomposition
- estimate instantaneous frequency and amplitude of mono-component signals
- implement linear deconvolution and nonlinear filtering methods
- use smartphones for telemedicine applications

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

DEBNATH, Lokenath. Wavelet Transforms and Their Applications. 2002. ISBN 3764342048. (EN)
HUANG, Norden E., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, 454.1971: 903-995. (EN)
JAN, Jiří. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 0852967608. (EN)
REYES ORTIZ, Jorge Luis. Smartphone-Based Human Activity Recognition. Springer, 2016. ISBN 9783319367705. (EN)
SALOMON, David. Data Compression: The Complete Reference. Springer Science & Business Media, 2007. ISBN 9781846286032. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MPC-BTB Master's 2 year of study, winter semester, compulsory
  • Programme MPC-BIO Master's 2 year of study, winter semester, compulsory
  • Programme MPAD-BIO Master's 2 year of study, winter semester, compulsory, fundamental theoretical courses of the profile core
  • Programme MPA-BIO Master's 2 year of study, winter semester, compulsory, fundamental theoretical courses of the profile core
  • Programme MPA-BTB Master's 2 year of study, winter semester, compulsory
  • Programme MPCN-BTB Master's 2 year of study, winter semester, compulsory
  • Programme MPAN-BIO Master's 2 year of study, winter semester, compulsory

  • Programme MPCN-BIO Master's

    specialization MPC-BIO_TECH , 2 year of study, winter semester, compulsory
    specialization MPC-SPORT_TECH , 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Lukáš Smital, Ph.D.

Syllabus


01-Course introduction
02-Sampling frequency conversion
03-Filters with sampling frequency conversion
04-Wavelet Transform - introduction
05-Wavelet Transform - wavelet filter design
06-Wavelet Transform - orthogonal filters
07-Wavelet Transforms + signal filtering
08-Wavelet Transforms + signal compression
09-Wavelet Transforms + ECG delineation
10-Use of a SmartPhone in telemedicine
11-Empirical Mode Decomposition
12-Instantaneous frequency/amplitude
13-Linear deconvolution, nonlinear filtration methods
14-The use of artificial intelligence in biological data processing  


Exercise in computer lab

18 hours, compulsory

Teacher / Lecturer

Ing. Lukáš Smital, Ph.D.

Syllabus


1. Repetition, signal processing in Matlab
2. Sampling frequency conversion
3. Project assignment + project kick off
4. WT in Matlab - wavelet toolbox, manual wavelet decomposition
5. WT in Matlab - manual wavelet reconstruction
6. WT in Matlab - signal filtering
7. WT in Matlab - signal compression
8. WT in Matlab - ECG delineation
9. Test + SmartPhone in telemedicine
10. - 12. Work on individual projects (individual consultation)
13. Project presentation + lab evaluation

Project

8 hours, compulsory

Teacher / Lecturer

Ing. Lukáš Smital, Ph.D.

Syllabus


An individual project implemented in the form of a competition is an integral part of the course, in which students aim to achieve the best possible results according to a selected evaluation criterion. The project topic changes every year and reflects current challenges in biomedicine and biological signal processing (e.g. respiratory rate estimation from ECG/PPG, signal quality assessment, sleep apnea detection, analysis of cardiac or respiratory signals).
Project assessment includes the functionality and quality of the implemented code, achieved results, algorithmic complexity and originality, code clarity, technical documentation, and project presentation. 

Součástí výuky je individuální projekt realizovaný formou soutěže, kde se studenti snaží dosáhnout co nejlepších výsledků dle zvoleného kritéria Téma projektu se každý rok obměňuje a reflektuje aktuální problémy biomedicíny a zpracování biologických signálů (např. odhad dechové frekvence z EKG/PPG, hodnocení kvality signálů, detekce spánkové apnoe, analýza srdečních či dechových signálů).
Hodnocení projektu zahrnuje funkčnost a kvalitu implementovaného kódu, dosažené výsledky, složitost a originalitu navrženého řešení, přehlednost kódu, technickou dokumentaci a prezentaci projektu. 
 


Individual preparation for a final exam

34 hours, optionally

Teacher / Lecturer

Ing. Lukáš Smital, Ph.D.

Individual preparation for excercises

34 hours, optionally

Teacher / Lecturer

Ing. Lukáš Smital, Ph.D.

Syllabus


Studenti a studentky mají k dispozici ke každému cvičení zpracované materiály ke studiu. Ty si samostatně prostudují předem a případně si dohledají informace v prezentacích k přednáškám nebo čerpají z jiných zdrojů. 


Students are provided with prepared study materials for each computer-based exercise. They are expected to study these materials independently in advance and, if necessary, look for additional information in lecture presentations or use other sources.