Signal Processing

The course cannot be taken simultaneously with:

Analýza signálů (A6M31ANS)

The course is a substitute for:

Analýza signálů (A6M31ANS)

Lecturer:

Pavel Sovka (gar.), Radoslav Bortel

Tutor:

Pavel Sovka (gar.), Radoslav Bortel, Pavel Máša

Supervisor:

Department of Circuit Theory

Synopsis:

Biomedical signal processing - principles and methods. Digitalization and quantization of biomedical signalsDigital filters in time domain. Digital filters in frequency domain. Decimation, interpolation and their use in parallel signal analysis. Short time Fourier transforms. Wavelet transformation and filter banks. Statistical analysis and biomedical signal modeling. Seminars are devoted to the implementations and simulations of the modern biomedical methods in MATLAB.

Requirements:

The elaboration of one selected problem is the condition for the credit.

Syllabus of lectures:

1. Biomedical signals - classification, properties

2. Biomedical signal representation in time and frequency domain

3. Digitalization and quantization of biomedical signals

4. Digital filters in time domain

5. Digital filters in frequency domain

6. Decimation, interpolation and their use in parallel signal analysis

7. Short time Fourier transform

8. Wavelet transformation and filter banks

9. Statistical analysis and biomedical signal modeling

10. HMM use for signal classification

11. Changepoint detection in biomedical signals

12. Coherence analysis principles

13. Biomedical adaptive signal processing

14. Adaptive noise enhancement in biomedical systems

Syllabus of tutorials:

1. Introduction to MATLAB and other tools

2. Basic operations in biomedical processing

3. Biomedical signal quantization - types, consequences

4. User digital filter realizations

5. Filter realization using DFT

6. Biomedical signal decimation and interpolation - realization

7. Basic functions used for short time spectral analysis

8. Filter bank implementation

9. Corelation, application for EEG and ECG signals

10. Examples of biomedical signals parameterizations

11. Examples of Bayesian changepoint detectors

12. Analysis EEG, EMG, R-R intervals, and breath using coherence

13. Realizations of predictor and estimator

14. Adaptive noise canceling and adaptive line enhancement in ECG

Study Objective:

Study materials:

1. Openheim, A.V., Shafer, R.W.: Discrete-Time Signal Processing. Prentice-Hall, Inc., New Jersey, 1998

2. Bendat, J., Piersol, A.: Random Data: Analysis Measurement Procedures, John Wiley & Sons, Ins., New York, 1971

Note:

Time-table for winter semester 2011/2012:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Fri
Thu	roomT2:A4-405 Bortel R. Máša P. 12:45–14:15 (lecture parallel1 parallel nr.101) Dejvice Laborator
Fri	roomT2:C3-52 Sovka P. 08:15–10:45 (lecture parallel1) Dejvice Posluchárna

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Biomedical Engineering- structured studies (compulsory course)