CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2022/2023
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

# Biomedical Data Analysis and Processing

Code Completion Credits Range Language
17PBBAZD KZ 2 1P+1C Czech
Garant předmětu:
Jan Kauler
Lecturer:
Lucie Horáková, Jan Kauler
Tutor:
Lucie Horáková, Jan Kauler
Supervisor:
Department of Biomedical Informatics
Synopsis:

Time series analysis, trends, mutual dependency, stationarity. Correlation function and covariance function. Algorithms of correlation function estimation. Impact of removing trends to autocorrelation function. Periodogram - relationship between corellogram and periodogram. Frequency spectrum, spectrum of random signals. Linear frequency filtering. AR, ARMA, and MA processes. Spectral analysis. FFT algorithm. Non-parametric methods of the frequency spectrum estimation. Positives and negatives of the specteal analysis. Repeated measurements and analysis of their properties. AR a ARMA model parameter identification. Prediction. Bivariance analysis of time series - cross-correlation and cross-covariance and their estimation. Bispectrum.

Requirements:

- examination: written test

Syllabus of lectures:

1. Time series analysis - fundamentals; trends, mutual dependency, stationarity. Correlation function and covariance function. Algorithms of correlation function estimation.

2. Impact of removing trends to autocorrelation function. Periodogram - relationship between corellogram and periodogram.

3. Frequency spectrum, spectrum of random signals. Linear frequency filtering.

4. AR, ARMA, and MA processes. Spectral analysis. FFT algorithm.

5. Non-parametric methods of the frequency spectrum estimation. Positives and negatives of the spectral analysis.

6. Repeated measurements and analysis of their properties.11. AR and ARMA model parameter identification.

7. Prediction. Bivariance analysis of time series - cross-correlation and cross-covariance. Estimation of cross-correlation and cross-covariance functions. Bispectrum.

Syllabus of tutorials:

1. Time series filtering (MA), time series decomposition.

2. Box-Jenkinson methodology.

3. Control test (25 points), interpolation and time series processing.

5. Cluster analysis.

6. Implementation of the fuzzy approximator.

7. Control test (25 points).

Study Objective:

to provide students with basic methods of statistical processing time series typical for life sciences

Study materials:

[1]Diggle P.J. Time Series. A Biostatistical Introduction. Clarendon Press. Oxford 1996

[2]Weiss S.M., Indurkhya N. Predictive Data Mining

Note:
Time-table for winter semester 2022/2023:
 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 roomKL:B-331Kauler J.08:00–09:50ODD WEEK(lecture parallel1)Kladno FBMILab. robotiky a asis. tech. roomKL:B-331Horáková L.08:00–09:50EVEN WEEK(lecture parallel1parallel nr.1)Kladno FBMILab. robotiky a asis. tech.
Time-table for summer semester 2022/2023:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2023-03-21
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet2171006.html