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Automatic detection of strict left bundle branch block
Němčáková, Jesika ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
The aim of this paper is to introduce the theory behind electrophysiology of hearth and pathology of left bundle branch. Furthermore, algorithms for automatic detection of left bundle branch block (LBBB) according to strict criteria are proposed. Algorithms are tested based on data from THEW databes within the Matlab interface. The final part of the bachelor’s thesis is devoted to the evaluation of the success of individual algorithms and the comparison of results on training data with test data.
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Detection of pathologies in ECG signals
Němec, Radek ; Mézl, Martin (referee) ; Kašpar, Jakub (advisor)
This thesis attempts with methods for detection of pathologies in ECG signals. The first part is focused on the basic theory of heart anatomy, electrocardiography and individual heart pathologies. The second part describes the preprocessing of ECG signals, detection of QRS complexes and detection of individual pathologies. In the third part the detector is being tested on CSE database and my own measured data.
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Software Tools for the Analysis of Cardiac Depolarization in Patients with Heart Failure
Smíšek, Radovan ; Richter, Aleš (referee) ; Nováková, Marie (referee) ; Kolářová, Jana (advisor)
Heart failure is a disease in which the heart cannot adequately supply the body with oxygenated blood. Approximately 2% of people in the developed world have heart failure, which is increasing. Cardiac resynchronization therapy, which involves the implantation of a biventricular pacemaker, is used in patients with ventricular dyssynchrony to suppress or at least reduce the symptoms of heart failure. The problem is that 30–50% of patients do not benefit from performed resynchronization therapy. The correct patient selection and the pacemaker implantation and set-up method influence the success rate. The thesis proposes methodologies and software to facilitate the selection of patients suitable for cardiac resynchronization therapy, select the correct pacing lead placement, and optimize pacemaker settings. The developed software is based on standard parameters used in clinical practice (QRS duration, presence of left bundle branch block, classification of heart rhythm) and, in addition, on the analysis of ultra-high-frequency ECG, which provides more detailed information on cardiac activation than the standard parameters used. The developed software helps patients in 16 hospitals in the Czech Republic and abroad.
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Detection of pathologies in ECG signals
Němec, Radek ; Mézl, Martin (referee) ; Kašpar, Jakub (advisor)
This thesis attempts with methods for detection of pathologies in ECG signals. The first part is focused on the basic theory of heart anatomy, electrocardiography and individual heart pathologies. The second part describes the preprocessing of ECG signals, detection of QRS complexes and detection of individual pathologies. In the third part the detector is being tested on CSE database and my own measured data.
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Detection of pathologies in ECG signals
Němec, Radek ; Mézl, Martin (referee) ; Kašpar, Jakub (advisor)
This thesis attempts with methods for detection of pathologies in ECG signals. The first part is focused on the basic theory of heart anatomy, electrocardiography and individual heart pathologies. The second part describes the preprocessing of ECG signals, detection of QRS complexes and detection of individual pathologies. In the third part the detector is being tested on CSE database and my own measured data.
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Detection of pathologies in ECG signals
Němec, Radek ; Mézl, Martin (referee) ; Kašpar, Jakub (advisor)
This thesis attempts with methods for detection of pathologies in ECG signals. The first part is focused on the basic theory of heart anatomy, electrocardiography and individual heart pathologies. The second part describes the preprocessing of ECG signals, detection of QRS complexes and detection of individual pathologies. In the third part the detector is being tested on CSE database and my own measured data.
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