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Segmentation of ECG signals based on their quality
Zobačová, Barbora ; Maršánová, Lucie (referee) ; Vítek, Martin (advisor)
This semestral thesis deals with methods for continuous estimation of the quality of the ECG signal. The theoretical part includes the functional anatomy of the heart, the basics of electrocardiography, the types of noise that can be found in the ECG records, and a description of several methods for the continuous estimation of the ECG signal quality. Next here are some approaches to segmenting ECG signals based on their quality. The practical part deals with the implementation of two methods. The first method is the SNR estimation method based on the Wiener filter. The second method is the method of segmentation of ECG signals based on their quality. Both methods were tested on artificial and real signals.
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Detection of ventricular arrhythmia in long-term ECG signals
Khaliullina, Sabina ; Smital, Lukáš (referee) ; Maršánová, Lucie (advisor)
Detection of premature ventricular contraction in long-term ECG signals is an important task in medicine. This work briefly describes the cardiac activity and the manifestations of ventricular extrasystoles in the ECG record. Methods are described for automatic detection of premature contractions. The main content of the work is the implementation of two selected methods in the MATLAB program environment, combining the classifier from the first method and parameters from the second method, testing and evaluating the success of the obtained results.
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Detection of atrial fibrillation in ECG
Húsková, Michaela ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
Aim of this thesis is description of problems of atrial fibrillation and methods that could be used for detection in the electrocardiogram. The introductory part of the theoretical analysis deals with the principle of electrophysiology of the heart and mainly the pathophysiology of atrial fibrillation. Additionally the work is focused on describing methods on automatic atrial fibrillation detection and capabilities of PhysioNet database. In the practical part methods are implemented in the MATLAB environment. After using the statistics to evaluate the quality of the parameters, the automatic classification of the data was performed by the method of The Nearest Neighbour. Finally, the accuracy of testing is presented.
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Use of higher-order cumulants for ECG analysis
Maršánová, Lucie ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This work deals with using higher order cumulants for analysis ECG. In the first part of work is described principle of electrocardiography, followed by matemathical derivation of higher order cumulants, description of their properties and their use in current practice. In the next part of paper is described caltulation higher order statistic of ECG beats in Matlab programming environment. In the practical part of work are tested predicted properties. Distinctive properties are minimalization of amplitude and time shift and Gaussian noise. This properties of higher order cumulants enable lesser variance of beats in one class and easier clasification ECG. Calculation of cumulants from real ECG beats of various groups is then realized. Classification based on the original ECG cycles and cumulants is performed using artificial neural network. Results of these classification approaches are then compared and discussed.
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Detection of atrial fibrillation in long-term ECG records
Imramovská, Klára ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The thesis deals with problems of automatic detection of atrial fibrillation in long-term ECG records. The preface of the theoretical part describes the electrophysiology of the heart and the principle of atrial fibrillation. Furthermore, it introduces methods of automatic detection of atrial fibrillation. In the practical part a method which uses the symbolic dynamics and a calculation of Shannon entropy is implemented in the MATLAB software environment. The method is tested on signals from the MIT-BIH Atrial Fibrillation Database and the Long-Term AF Database. Lastly, the accuracy of the classification is compared with methods described in different papers.
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Detection of ventricular premature beats in long-term ECG
Šagát, Martin ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The thesis deals with problems of premature ventricular complexes and ways of their detection. Heart’s basic physiological activities and the principle of their measurement are described in the theoretical part of the thesis. It deals specifically with the appearance and manifestations of ventricular extrasystoles in the ECG. The thesis also discusses various ways of detecting premature ventricular complexes. In this thesis, detection based on morphological features, wavelet transform and signal energy was used. All methods were implemented using Matlab and tested on all signals of the MIT-BIH Arrhythmia database.
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Detection of atrial fibrilation in long-term ECG
Polcer, Simon ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The bachelor’s thesis deals with the automatic detection of atrial fibrillations in the long-term ECG signals. First, it provides a description of the electrophysiology of the heart, the atrial fibrillation and the automatic methods of their detection. The first method, implemented in this work, is based upon the parameters that were calculated from the irregularities of the RR intervals. The second method uses the stationary wavelet transform and other parameters are computed after the signal transformation. The calculated parameters are subsequently statistically evaluated in the STATISTICA software. Parameters are assessed by the non-parametric Mann-Whitney test, which selects parameters that exhibit statistically significant differences between signals containing atrial fibrillation and sinus rhythm. At the end, the classification is performed by two approaches such as Support vector machine and k-Nearest Neighbours.
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Detection Of P Wave During Second-Degree Atrioventricular Block In Ecg Signals
Maršánová, Lucie ; Němcová, Andrea ; Smíšek, Radovan
Automatic detection of P wave during the second-degree AV block is the main condition for automatic detection of this pathology. This work deals with developing of the algorithm for P wave detection. The algorithm is appropriate for ECG signals with AV block as well as signals with other rhythm types (it does not produce false positive P wave detections). For P wave detection, the phasor transform is applied and several innovative rules are created. These rules are based on knowledge of heart manifestation during both physiological and pathological heart function. The proposed algorithm consists of four parts – filtration, QRS complex detection, application of rules, and P wave detection. The accuracy of the P wave detection algorithm is 99.74 % for signals with AV block, and 99.82 % for signals without any pathologies.
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Detection of ventricular arrhythmia in long-term ECG signals
Khaliullina, Sabina ; Smital, Lukáš (referee) ; Maršánová, Lucie (advisor)
Detection of premature ventricular contraction in long-term ECG signals is an important task in medicine. This work briefly describes the cardiac activity and the manifestations of ventricular extrasystoles in the ECG record. Methods are described for automatic detection of premature contractions. The main content of the work is the implementation of two selected methods in the MATLAB program environment, combining the classifier from the first method and parameters from the second method, testing and evaluating the success of the obtained results.
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Detection of atrial fibrillation in long-term ECG records
Imramovská, Klára ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The thesis deals with problems of automatic detection of atrial fibrillation in long-term ECG records. The preface of the theoretical part describes the electrophysiology of the heart and the principle of atrial fibrillation. Furthermore, it introduces methods of automatic detection of atrial fibrillation. In the practical part a method which uses the symbolic dynamics and a calculation of Shannon entropy is implemented in the MATLAB software environment. The method is tested on signals from the MIT-BIH Atrial Fibrillation Database and the Long-Term AF Database. Lastly, the accuracy of the classification is compared with methods described in different papers.
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