SubjectsSubjects(version: 948)
Course, academic year 2023/2024
  
Biomedical Signals - N445083
Title: Biomedicínské signály
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2021
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, MC [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type:  
Guarantor: Čmejla Roman prof. Ing. CSc.
Examination dates   Schedule   
Annotation -
Last update: VYSATAO (23.08.2013)
Principles and methods of recording, digital processing and interpretation of biological signals: the methodology of recording, detection and removal of artifacts, parameters of signals digitalization, parallel multi-channel processing of various biological signals, the interpretation of the strengths and weaknesses of linear and nonlinear processing methods (Fourier, wavelet, Prony's transformation estimates energy signals, similarity, estimates of complexity).
Aim of the course -
Last update: VYSATAO (23.08.2013)

Students completing the course will be able to capture and process the basic biological single and multi-channel signals (ECG, EEG, blood pressure, EMG, EP, ventilation), 2D and 3D images of CT, MR, ultrasound, OCT. They will conduct experiments on Vernier equipment (measuring temperature, pressure, ECG, ventilation parameters and muscle activity) and Walter (EEG). They will be able to filter signals, estimate power spectra using FFT, wavelet analysis, parameterization similarities signals to estimate the entropy and fractal dimension of signals and images.

Literature -
Last update: TAJ445 (30.09.2013)

R: Uhlíř, J., Sovka, P., Čmejla, R.: Úvod do číslicového zpracování signálů. ČVUT Praha 2003. ISBN 80-01-02613-2

R: Zaplatílek, K., Doňar, B.: MATLAB - začínáme se signály. BEN 2006. ISBN 80-7300-200-0

A: John G. Proakis: Digital Signal Processing (4th Edition) ISBN-10: 0131873741; ISBN-13: 978-0131873742

Learning resources -
Last update: TAJ445 (30.09.2013)

none

Syllabus -
Last update: VYSATAO (23.08.2013)

1. Why study biological signals, the types of signals and their genesis, the most common applications.

Exercise: Taking biosignals using Vernier Labquest devices, imaging and signal processing by the programs Logger Lite and Logger Pro

2. Recording and properties of the biomedical data, information selection, stationarity, artifacts, denoising

Tutorial: Signal processing toolbox in Matlab, generating signals and digital signal processing, environment SPTOOL

3. Signal analysis of a muscle fiber, motor unit evaluation, neuromuscular function testing, orthogonal function decomposition algorithms for interference curve

Exercise: Scanning signals of motor units by surface electrode , a EMG signal decomposition, decomposition of multichannel recording

4. Signal analysis of motor, sensory and autonomic peripheral nerve fibers, analysis of spinal reflexes and F waves, parameterisation of measured data

Exercise: Expert systems for recognition axonal and demyelinating polyneuropathy based on automatically parameterized curves using neural networks

5. Analysis of somatosensory, visual, auditory and cognitive evoked potentials, methods of noise suppression, habituation and averaging, the problem of normal values

Exercise: Detection of evoked responses of individual records, multiresolution decomposition methods, discriminant analysis

6. Analysis of normal and stress electrocardiogram

Exercise: Capturing and processing normal and stress electrocardiogram on the unit Labquest and Matlab

7. Analysis of the normal EEG, convolution, correlation, digital filtering

Exercise: Recording EEG on Walter device, filtering, signal detrending procedure, correlation analysis, spectral analysis

8. Analysis of stimulated EEG, evoked potentials induced by light, EEG classification, segmentation, wavelet transform, the degree of similarity of signals

Exercise: Recording a simulated EEG on Walter device, comparison of correlation, coherence and mutual information between close and distant channels in stimulated and non-stimulated EEG

9. Analysis of the 1-D and 2-D ultrasound signals, the spatial filtering and averaging using robust spatial median, using convolution, edge detection, linear morphological operations, texture

Exercise: Identification of anatomical structures in ultrasound images

10. Image processing of the computer tomography, Radon and inverse Radon transform, spatial transformations and image registration

Exercise: Segmentation and classification of segments for the CT images of the spine, spatial transformation and registration of brain CT images

11. Analysis of images of nuclear magnetic resonance, basic principles, functional magnetic resonance imaging

Exercise: Viewing and processing of MR images in image procesing toolbox in Matlab

12. Analysis of digital subtraction angiography images, magnetic resonance and computed tomographic angiography,

Exercise: 3D reconstruction of the vascular tree from 2D images

13. Analysis of single photon emission images tomography, positron emission tomography, CT and MR images with contrast, morphological filtering

Exercise:

14. Methods of compression and decompression, biosignal recording and transmission, long-term recording of biosignals

Exercise: Compression and decompression of images, comparing compression using wavelet transform, sequence length coding, Huffman coding, arithmetic coding, delta encoding, JPEG

Registration requirements -
Last update: VYSATAO (23.08.2013)

none

Teaching methods
Activity Credits Hours
Obhajoba individuálního projektu 1 28
Účast na přednáškách 1 28
Práce na individuálním projektu 1 28
Účast na seminářích 1 28
4 / 4 112 / 112
Coursework assessment
Form Significance
Regular attendance 40
Defense of an individual project 20
Report from individual projects 20
Continuous assessment of study performance and course -credit tests 20

 
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