March 19^{th} 2020 - 17ABBBLS course runs in distance learning mode to reduce the risk of coronavirus infection - see instructions in section 'Others' below (requirements for tutorials and exam are included) and lectures for distance learning in section 'Lectures'.
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
17ABBBLS | Z,ZK | 4 | 2P+2C | English |
The subject deals with origins and description of the most important electric and non-electric biological signals. The principles of generation, recording and basic properties are studied in all the signals. The studied signals involve native and evoked biosignals, including biological signals of the heart, brain, muscles, nervous system, auditory signals, visual system, signals from the gastro-intestinal system etc. Advanced methods of digital biosignal processing,spectrum analysis, modern methods of artificial intelligence, features extraction, automatic classification, graphic presentation of results. Adaptive segmentation, artificial neural networks for signal procesing.
Basic knowledge of MATLAB, C++ or other object-oriented language is an advantage, but it is not strictly required.
Exercises:
Exam:
A. You can not take the exam without getting a credit (exercises) and enrollment in the KOS.
B. The exam consists of a written test, where ABC (one correct one) - 1 point and a 5-point answer (very important questions) is combined. It is possible to get a maximum of 70 points from the test. The student must earn at least 50 % of the exam points (35 points).
Overall rating of the course:
A. See the ECTS grading scale. 100 points are divided between the parts as follows: maximum 30 % for the obtained credit and maximum 70 % for the passed test/exam.
B. The minimum score is 50. The student must gain at least 15 points from the exercise and a minimum of 35 points per exam.
C. Bonus for successful solving of optional tasks - 10 points / task = max. 40 points.
The overall assessment is carried out according to the ECTS table given in the CTU:
100-90 points: A, great
89-80 points: B, very good
79-70 points: C, okay
69-60 points: D, satisfactory
59-50 points: E, enough
less than 50 points: F, insufficient
1. Introduction to digital biosignal processing. Motivation. Basic characteristics of EEG, EKG, EOG, EMG. Basic graphoelements in EEG, polysomnography, hypnogram. Polysomnography. Artefacts.
2. Statistic and probabilistic signal properties. Probability distribution. Stochastic processes and time series analysis. Convolution, impulse characteristics. Mean, standard deviation, correlation analysis. Cross-correlation function. The nonstationary behaviour of EEG. Frequency bands.
3. Biological signals recording and preprocessing. Digital EEG devices. Basic sequence of signal transfer into computer. A/D converter, differential amplifiers. Analog and digital filters. Problems of sampling and quantization, Nyquist theorem and sampling frequency. Errors during signal conversion. Signal conditioning, aliasing in the time and frequency domains. Digital and frequency aliasing. Denoising a detrending. EEG machine calibration.
4. ECG, method of measurement and basic signal characteristics. EOG, method of measurement and basic signal characteristics.
5. EMG, method of measurement and basic signal characteristics. Multimodal monitoring.
6. Evoked potentials, VEP, AEP, SEP, BAEP, MEP.
7. Fourier transformation. Discrete FT. Fast FT (FFT). Principles of computing. Decimation in time and frequency. FFT butterfly. Special algorithms of computing. Inverse transform. Signal analysis and synthesis. Spectrum estimation. Filtering using FFT. Digital filters for biosignal analysis. FIR and IIR filters, properties. Linear and nonlinear phase characteristics. Types of filters, band pass, low pass, high pass, notch filters. Simple methods of design. Example of design using FFT (window method). Examples of application to real and simulated signal.
8. Spectrum analysis. Power spectral density. Periodogram. Parametric and non-parametric methods of spectral analysis. Practical problems of spectrum estimation. CSA
9. Multichannel adaptive segmentation. Motivation. Non-stationarity of biosignals. Basic methods. Multi-channel on-line adaptive segmentation. Extraction of symptoms. The parameter settings. Advantages and limitations of methods. Other segmentation algorithms.
10. Methods of automatic classification. Basic algorithms of cluster analysis. K-means algorithm. Optimal number of classes. Limits and constraints of cluster analysis. Fuzzy cluster analysis.
11. Density-based classification methods. Instance-based learning methods. K-NN classification. Fuzzy k-NN. Practical examples of classification methods for biological signals.
12. Simple methods for automatic epileptic spikes detection.
13. Topographic mapping of electrophysiological activity. Visualization. Principle of brain mapping. Amplitude and frequency brain mapping. Interpolation. Direct and inverse task. Use in clinical diagnostics.
14. Metrics. Data normalization. Statistical data processing.
1. Artefacts in biosignal recording. Measurement of the electrical properties of the recording electrodes.
2. Measurement on ECG.
3. Measurement on EMG.
4. Measurement on EEG.
5. Measurement of tendon jerks.
6. Recording of evoked EEG potentials.
7. Audiometric measurements.
Conditions for credit
A. Participation in the exercises, max. 1 unexcused absence
B. Submission of the measurement protocols
C. Presentation of the selected topic (5-10min, PowerPoint)
D. Passing the test at the end of the semester with questions from the practical measurements
The aim of the subject is to get to know the basic biological signals of electric and non-electric origin, methods of their recording protecting their diagnostic values.
The basic and advanced methods of biosignal processing will be discussed.
[1] Sormno L, Laguna P, Bioelectrical Signal Processing in nurological and cardiological applications, Elsevier,2005
[2] Bruce, E.N. Biomedical Signal Processing and Signal Modelling.New York, J.Willey & sons 2001.
[3] Baura G.D. System Theory and Practical Applications of Biomedical Signals.Piscataway, IEEE Press 2002.
[4] Krajca V., Mohylova J. Biologicke signely. e-learning www.skolicka.fbmi.cvut.cz, password signaly
[5] MIKE X. COHEN. Analyzing neural time series data: theory and practice. 2014. ISBN 0262019876.
Attachment | Size |
---|---|
Lectures for distance learning 2020 | 21.69 MB |
Lectures 2018 all | 37.13 MB |
Lecture 00 - Introduction | 187.87 KB |
Lecture 01 - Biological signals | 670.89 KB |
Lecture 02 - Biological parameters | 887.1 KB |
Lecture 03 - Preprocessing of the biosignals | 252.15 KB |
Lecture 04 - ECG, EOG | 2.55 MB |
Lecture 06 - Evoked potentials | 2.12 MB |
Lecture 10, Lecture 11 - Supervised and unsupervised learning | 449.37 KB |
Lecture 13 - Topographic mapping | 3.09 MB |
Lecture 14 - Metrics, normalisation and statistics | 1.31 MB |
EMG - Introduction | 3.67 MB |
Attachment | Size |
---|---|
IMPORTANT: Instructions for distance learning 2020 | 24.67 KB |
IMPORTANT: Schedule for lectures for distance learning 2020 | 35.54 KB |
IMPORTANT: Schedule for tutorials-labs for distance learning 2020 | 356.43 KB |
Schedule of tutorials-labs 2019/2020 | 368.44 KB |
Principal of the convolution in time domain | 115.91 KB |
Optional task n. 1 - ECG | 168.58 KB |
Optional task n.1 - dataset of the ECG | 118.42 KB |
Optional task n. 2 - Fourier transform | 235.08 KB |
Optional task n. 3 - Filter Design | 125.22 KB |
Optional task n. 3 - dataset NoisyECG | 337.96 KB |
Optional task n.4 - Topographic mapping | 130.47 KB |
Optional task n.4 - dataset (electrodes positions) | 1.05 KB |
Protocol template | 743.25 KB |