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Rules for Determination of Expected P Wave Type in ECG Signals
Maršánová, Lucie
The aim of this work is to improve existing possibilities of automatic detection of pathological P wave in electrocardiogram. For detection of different types of P wave is crucial to choose suitable algorithm. In this paper, establishing of rules for determining which P wave type is in given situation probably present is done. This rules are derived from positions of QRS complexes. Therefore, accurate QRS detector based on a signal filtering by bandpass filter and finding the maximum in a moving window is designed.
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Analysis of experimental ECG
Maršánová, Lucie ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.
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Delineation of ECG signals using phasor transform
Koupil, Michal ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
TThis project deals with delineation of ECG signals using phasor transform. This method is rather new in the field of automatic delineation of ECG and it is not widely used in practice yet. In the theoretical part, the relation between heart activity and ECG is described. A phasor transform is also described. In the practical part, a program was implemented in the computing environment of MATLAB. Its purpose is to delineate the signals. At first, it detects waves’ peaks, then its beginning and starting points. In the next part, analysis of the results is done, as well as comparison with other authors. The testing was performed on the QT signals from the Physionet database.
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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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