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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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EEG Signal Analysis based on EMD and Discrete Energy Separation Algorithm
Potočňák, Tomáš
This paper deals with spectral analysis of nocturnal EEG signal from apnoea/hypopnea patients. Main goal is to employ methods independent to Fourier Transform, because of nonstationary character of signal, to better description of frequency changes. For this purpose, analysis based on Empirical Mode Decomposition and Discrete Energy Separation Algorithm was tested. This method is similar to commonly used Hilbert Huang Transform, but can provide higher time and frequency resolution due to algorithms based on Teager-Keiser Energy Operator, which can work with very short time window.
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Korelační analýza akciových indexů pomocí Empirical Mode Decomposition
Ulyanin, Alexey ; Černý, Michal (advisor) ; Formánek, Tomáš (referee)
This thesis studies dependence of ?nancial time series, represented by stock indices of geographically separated economics. Daily prices of selected stock indices from 01.05.1988 to 20.04.2017 are used for the analysis. In the fi?rst section the dataset is described. The second section goes through methodology for the analysis, including empirical mode decomposition (EMD) algorithm, with the help of which the initial time series can be transformed into a few time series, that are mostly independent on each other, for further analysis (for example, correlation analysis). EMD is an interesting method of signal processing, which may help to look at the time series analysis from a different perspective. The thesis should extend the work of Guhathakurta et al. (2008) and extend the time scale and number of indices. Correlation analysis is also performed on the initial time series and the transformed ones. The aim of this thesis is to prove, whether stock indices of geographically separated economics have similarities in their behavior and test whether they are dependent on each other, by using methods from Guhathakurta et al. (2008) extended by correlation analysis.
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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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Detrending heart rate variability signal with empirical mode decomposition EMD
Foltová, Anežka ; Janoušek, Oto (referee) ; Kubičková, Alena (advisor)
HRV analysis is an important indicator of pathophysiological examination. R-R waves are used for detection and analysis of ECG interval. R-R intervals can be analyzed by various methods. During spectral analysis is an often phenomenon disturbing non-stationary trend, which needs to be removed. In this paper, which deals about detrending, is mainly introduced Empirical mode decomposition (EMD) which is popular in recent years. Subsequently, this method is being compared to the method of wavelet transformation and Smoothness prior apprach (SPA).
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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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Using Hilbert Huang transformation for analysis of non-stationary signals from physical experiments
Tuleja, Peter ; Balík, Miroslav (referee) ; Rášo, Ondřej (advisor)
This paper discusses the possible use of Hilbert-Huang transform to analyze the data obtained from physical experiments. Specifically for the analysis of acoustic emission in the form of acoustic shock. The introductory section explains the concept of acoustic emission and its detection process. Subsequently are discussed methods for signal analysis in time-frequency domain. Specifically, short-term Fourier transform, Wavelet transform, Hilbert transform and Hilbert-Huang transform. The final part contains the proposed method for measuring the performance and accuracy of different approaches.
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Preliminary Acoustic Analysis of Noise Components in Patients with Parkinson's Disease
Galáž, Z.
This paper deals with acoustic analysis of noise components extracted from speech signals of patients with Parkinson’s disease (PD) who recited a poem. Experimental dataset consisted of 97 PD patients with different disease progress and 55 healthy controls (HC). The analysis is based on parametrization of 2 rhymes recitation using dysphonia features. We obtained classification accuracy 76.66% for female speakers, 69.65% for male speakers and 69.24% for the mixture of both genders.
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