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Methods for respiration estimates from ECG signal
Mitrengová, Jana ; Mézl, Martin (referee) ; Králík, Martin (advisor)
The thesis deals with the realization of methods for estimation of the respiratory curve from the ECG signal. The first part of the thesis deals with the anatomy and physiology of the respiratory and cardiovascular system. In this part of the thesis are also described ways of the breathing monitoring. The second part of the thesis is dedicated to the description of individual methods for the ECG derived respiration. The third part deals with the realization of selected methods, application of method algorithms on real data and comparison of resulting respiratory curves with the respiratory signals available from the PhysioNet database. In conclusion, the individual methods are compared with each other.
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ECG baseline wander correction based on the empirical mode decomposition
Šlancar, Matěj ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.
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Advanced analysis of signals from gait laboratory.
Húsková, Michaela ; Mézl, Martin (referee) ; Svozilová, Veronika (advisor)
The aim of the thesis is a realization of advanced analysis of signals from gait laboratory. The introductory part deals with the gait cycle and its relation to the joints kinematic is discussed. Additionally, the work is focused on the description of the gait laboratory and the definition of the indexes in order to quantify patient´s overall gait in kinematic analysis. In the practical part, kinematic data analysis was implemented in the MATLAB environment and the results of healthy individuals and patients with cerebral palsy were compared. Kinematic analysis included peak detection in specific kinematic variables. In the last part a graphical user interface for visualization was implemented.
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Removing baseline wander in ECG with empirical mode decomposition
Procházka, Petr ; Kolářová, Jana (referee) ; Kubičková, Alena (advisor)
In this semestral thesis, realizations of chosen linear filters for baseline wander are described. These filters are then used on artificial ECG signals from CSE database with added baseline wander. These methods are compared and results are evaluated. After that, literature search of Empirical mode decomposition method is utilized. Realization of designed filters in MATLAB programming language are described, then results are evaluated with respect to filtration success.
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Signal Decomposition using EMD transform
Mžourek, Zdeněk ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
Aim of this thesis is to introduce empiric mode decomposition as a method for decomposing arbitrarily nonlinear and non-stationary signal into intrinsic mode functions. Using empiric modal decomposition together with Hilbert transform produces instantaneous frequency. We can use this instatenous frequency to create a Hilbert spectrum and use it for analysis in time-frequency domain. In next part we show several drawbacks of this method. We also present several ways how to improve empirical mode decomposition algorithm to obtain better results. An example of decomposition by empiric mode decompositon is made to illustrate how the whole procedure works.
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Research of the new augmentation methods for online handwriting
Sigmund, Jan ; Burget, Radim (referee) ; Zvončák, Vojtěch (advisor)
Graphomotor difficulties of school-aged children are characterised by problems in handwriting and drawing and can lead to developmental dysgraphia. Timely clinical diagnosis is critical to provide preventive care. In practice however, it is not feasible on day-to-day basis due to the need for expert staff and the prevalence of difficulties up to 30\%. Machine learning models can serve as an accessible objective tool for evaluating graphomotor functioning. In most cases there is not enough data collected, which results in poor classification performance. Therefore, this thesis focuses on data augmentation of online handwriting. Generating artificial samples is based on recombination of intrinsic mode functions, obtained by empirical mode decomposition. IMFs of health controls, numbering 72, and with graphomotor difficulties, 94 children in total, are calculated. The decomposition is performed specifically on X and Y coordinate time series. IMFs of the same indices of different subjects are randomly interchanged, thus producing a new signal. Then, the graphomotor features of the original and artificial time series are extracted. Only the spatial ones related to the coordinates are selected. Finally, the correlations of the features of the two databases will be analyzed and compared.
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Advanced analysis of signals from gait laboratory.
Húsková, Michaela ; Mézl, Martin (referee) ; Svozilová, Veronika (advisor)
The aim of the thesis is a realization of advanced analysis of signals from gait laboratory. The introductory part deals with the gait cycle and its relation to the joints kinematic is discussed. Additionally, the work is focused on the description of the gait laboratory and the definition of the indexes in order to quantify patient´s overall gait in kinematic analysis. In the practical part, kinematic data analysis was implemented in the MATLAB environment and the results of healthy individuals and patients with cerebral palsy were compared. Kinematic analysis included peak detection in specific kinematic variables. In the last part a graphical user interface for visualization was implemented.
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Methods for respiration estimates from ECG signal
Mitrengová, Jana ; Mézl, Martin (referee) ; Králík, Martin (advisor)
The thesis deals with the realization of methods for estimation of the respiratory curve from the ECG signal. The first part of the thesis deals with the anatomy and physiology of the respiratory and cardiovascular system. In this part of the thesis are also described ways of the breathing monitoring. The second part of the thesis is dedicated to the description of individual methods for the ECG derived respiration. The third part deals with the realization of selected methods, application of method algorithms on real data and comparison of resulting respiratory curves with the respiratory signals available from the PhysioNet database. In conclusion, the individual methods are compared with each other.
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