CTU FEE Moodle
Biological Signal Processing
B232 - Summer 23/24
Biological Signal Processing - XP31ZBS
| Credits | 4 |
| Semesters | Winter |
| Completion | Exam |
| Language of teaching | English |
| Extent of teaching | 2P+2C |
Annotation
The course deals with the processing of biosignals and advanced methods of processing resulting from current research in solving common projects in cooperation with top institutions (medical faculties, institutes of the ASCR, foreign universities). The subject concept allows us to respond flexibly to new directions and knowledge in the field.
Study targets
The aim of the study is to gain knowledge of advanced methods of biological signal processing.
Course outlines
I. Signal processing algorithms
1. Heart signals
2. Brain signals
3. Signals of muscles and nerves
4. Eye signals and sleep medicine
5. Voice and speech
6. Processing of less common biosignals (electrogastrogram, lung volumes and capacities, ...)
II. Selected lectures on advanced signal processing (will be adapted to the number and interests of students)
7. Detection and localization of signal changes using Bayesian methods
8. Quantitative evaluation of EEG in epileptology
9. Methods of detection of high-frequency oscillations in invasive EEG
10. Detection of interictal epileptoform discharges
11. Modeling connectivity in neuroscience
12. Analysis of pathological speech in neurology
13. Statistical analysis of speech
14. Surface electromyography in sport
1. Heart signals
2. Brain signals
3. Signals of muscles and nerves
4. Eye signals and sleep medicine
5. Voice and speech
6. Processing of less common biosignals (electrogastrogram, lung volumes and capacities, ...)
II. Selected lectures on advanced signal processing (will be adapted to the number and interests of students)
7. Detection and localization of signal changes using Bayesian methods
8. Quantitative evaluation of EEG in epileptology
9. Methods of detection of high-frequency oscillations in invasive EEG
10. Detection of interictal epileptoform discharges
11. Modeling connectivity in neuroscience
12. Analysis of pathological speech in neurology
13. Statistical analysis of speech
14. Surface electromyography in sport
Exercises outlines
The content of the exercises corresponds with the content of the lectures.
Literature
[1] Rangaraj M. Rangayyan: Biomedical Signal Analysis, Wiley-IEEE Press, 2015
[2] Vyas, N., Khalid, S.: Biomedical Signal Processing, Laxmi Publications Pvt Ltd, 2017
[3] Kayvan Najarian: Biomedical Signal and Image Processing, Crc Pr Inc, 2012
[4] Sornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, 2005
[5] Naik, Ganesh R.: Applications, Challenges, and Advancements in Electromyography Signal Processing, IGI Global, 2014
[2] Vyas, N., Khalid, S.: Biomedical Signal Processing, Laxmi Publications Pvt Ltd, 2017
[3] Kayvan Najarian: Biomedical Signal and Image Processing, Crc Pr Inc, 2012
[4] Sornmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, 2005
[5] Naik, Ganesh R.: Applications, Challenges, and Advancements in Electromyography Signal Processing, IGI Global, 2014
Requirements
The condition for granting credit is elaboration of selected task.