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Analysis of Ventricular Repolarization Parameters
Abbrent, Jakub ; Novotná, Petra (referee) ; Hejč, Jakub (advisor)
This bachelor’s thesis deals with the analysis of ventricular repolarization parameters on experimental ECG records. In the beginning of the theoretical part there are included information about heart electrophysiology, fundamental principle of ECG and cellular basis of the T-wave formation. Next chapter is focused on methods used for the analysis of ventricular repolarization, especially spatial parameters including principal component analysis (PCA). Then, in the thesis, there is described the database of experimental ECG signals created from isolated rabbit hearts. In the practical part of this bachelor’s thesis, there are implemented spatial parameters on experimental ECG records. Implementation of algorithms is performed after initial data preparation. Then, there is performed analysis of relation between spatial and hemodynamic parameters and the relation is evaluated by statistical analysis.
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Identification of supraventricular tachycardia segments using multiple-instance learning
Abbrent, Jakub ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
Supraventricular tachycardias have a high incidence in the population and often cause health disorders. The aim of this thesis is to automatically detect and localize atrial fibrillation in ECG records. The algorithm, implemented in Python, uses a convolutional neural network ResNet for detection with multiple-instance learning and decision rules. The output of the detection in the form of a feature signal is used for localization. The classification achieved F1 score of 0.87 on the test dataset. Then, paroxysmal atrial fibrillation was localized with a deviation of -0.40±2.26 seconds for the onsets and 1.09±2.75 seconds for the terminations of the episodes. Lastly, the obtained results are evaluated and discussed.
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Identification of supraventricular tachycardia segments using multiple-instance learning
Abbrent, Jakub ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
Supraventricular tachycardias have a high incidence in the population and often cause health disorders. The aim of this thesis is to automatically detect and localize atrial fibrillation in ECG records. The algorithm, implemented in Python, uses a convolutional neural network ResNet for detection with multiple-instance learning and decision rules. The output of the detection in the form of a feature signal is used for localization. The classification achieved F1 score of 0.87 on the test dataset. Then, paroxysmal atrial fibrillation was localized with a deviation of -0.40±2.26 seconds for the onsets and 1.09±2.75 seconds for the terminations of the episodes. Lastly, the obtained results are evaluated and discussed.
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Analysis of Ventricular Repolarization Parameters
Abbrent, Jakub ; Novotná, Petra (referee) ; Hejč, Jakub (advisor)
This bachelor’s thesis deals with the analysis of ventricular repolarization parameters on experimental ECG records. In the beginning of the theoretical part there are included information about heart electrophysiology, fundamental principle of ECG and cellular basis of the T-wave formation. Next chapter is focused on methods used for the analysis of ventricular repolarization, especially spatial parameters including principal component analysis (PCA). Then, in the thesis, there is described the database of experimental ECG signals created from isolated rabbit hearts. In the practical part of this bachelor’s thesis, there are implemented spatial parameters on experimental ECG records. Implementation of algorithms is performed after initial data preparation. Then, there is performed analysis of relation between spatial and hemodynamic parameters and the relation is evaluated by statistical analysis.
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