Course detail

Analysis and interpretation of biological data

FEKT-NABDAcad. year: 2019/2020

Electrocardiography (ECG): ECG signal – generation, leads, filtering of ECG signals, detection of QRS complex and delineation of ECG signals, compression of ECG signals, Heart Rate Variability (HRV) analysis.
Electroencephalography (EEG): Signal acquisition, pre-processing and artefacts removal, spectral analysis, sleep EEG.
Genomic signals: DNA sequence transformation and processing, phase representation


Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

Design and implementation of methods for preprocessing, analysis and interpretation of the most important biological signals.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The course provides in-depth treatment of analysis, processing and interpetation of biological signals, namely signals of the cardiovascular system and neurophysiological signals.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Svatoš,J.: Biologické signály I, geneze, zpracování a analza. Skriptum ČVUT, Praha 1992. (CS)
Tompkins, W.J.: Biomedical Digital Signal Processing, C-Language Examples and Laboratory Experiments for IBM PC. Prantice-Hall, Englewood Cliffs, 1993. (EN)
Bronzino, J.D. (Ed.): The Biomedical Engineering Handbook. CRC Press, 1995. (EN)
Rangayyan,R.M.: Biomedical Signal Analysis. IEEE Press, John Wiley &Sons, 2002. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-MN Master's

    branch MN-BEI , 1. year of study, winter semester, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus

Signals of the cardiovascular system. Electrocardiogram (ECG), time domain and frequency domain characteristics of ECG, vectorcardiogram, mapping.
Rest and stress ECG - processing and interpretation. Recommendations for standardization.
Linear, adaptive and nonlinear methods for suppression of disturbances in ECG (baseline drift, power-line interferences and myopotetials).
Waves detection, measured parameters and diagnostic systems for automated rest ECG classification.
Ventricular late potentials, T-wave alternans - standards for processing and analysis.
Monitored ECG (Holter, bed-side) - processing, typical data compression, analysis.
Fetal ECG, methods of FECG extraction from mother ECG. Phonocardiogram - characteristics and processing. Blood pressure curve (AVG).
Analysis of the heart rate variability in frequency domain.
Neurophysiological signals: EEG - signal characteristics, rhythms and typical isolated waveforms, noise and artifacts. Spectral analysis of EEG.
EEG analysis in time domain. Correlation analysis of EEG. Spike and sharp wave detection.
Evoked potentials - features, noise suppression, interpretation.
Other biomedical signals: EMG, features and analysis. Signals of respiratory system, signals of digestive system.
Data storage and transfer. Lossy and lossless data compression.

The other activities

13 hours, optionally

Teacher / Lecturer

Syllabus

Spectral analysis of the ECG signals.
Linear methods for suppression of baseline drift and power-line interferences in ECG.
Nonlinear methods for suppression of baseline drift and power-line interferences in ECG.
Averaging for suppression of myopotentials in stress ECG signals.
Filtering of stress ECG signals.
ECG QRS detection.
Analysis of the heart rate variability in frequency domain.
EEG analysis in frequency domain.
EEG analysis in time-frequency domain.
Filtering of evoked potentials.
Lossy compression of biosignals.