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Detection of poorly differentiated cardiac arrhythmias
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of atrial fibrilation, atrial flutter and sinus rhythm from ECG. Thesis also concentrate on the description of this arrhythmias and the learning algorithms used. In this thesis are implemented several classification approaches. For extraction of features is used convolution neural network and classification artifitial neural network. Selected 1D CNN method achived classification accuracy global F1 - score is 91 %. Moreover, the proposed CNN optimized with GA appears to be fast shallow network with better accuracy than the deep network. Created model are used for classification other type of arrhythmias too.
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Atrial fibrillation model
Ředina, Richard ; Smíšek, Radovan (referee) ; Ronzhina, Marina (advisor)
The aim of this master thesis is to create a 3D electroanatomical model of a heart atria, which would be able to perform atrial fibrillation. To control the model, the differential equations of the FitzHugh-Nagumo model were chosen. These equations describe the change of voltage on the cell membrane. The equations have established parameters. The modification of them leads to changes in the behavior of the model. The simulations were performed in the COMSOL Multiphysics environment. In the first step, the simulations were performed on 2D models. Simulations of healthy heart, atrial flutter and atrial fibrillation were created. The acquired knowledge served as a basis for the creation of a 3D model on which atrial fibrillation was simulated on the basis of ectopic activity and reentry mechanism. Convincing results were obtained in accordance with the used literature. The advantages of computational modeling are its availability, zero ethical burden and the ability to simulate even rarer arrhythmias. The disadvantage of the procedure is the need to compromise between accuracy and computational complexity of simulations.
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Detection of paroxysmal atrial fibrillation and atrial flutter
Krmela, Jan ; Němcová, Andrea (referee) ; Smíšek, Radovan (advisor)
This bachelor thesis deals with the problem of atrial fibrillation and flutter, the pathophysiology of these arrhythmias and their automatic detection. It includes a theoretical introduction necessary to understand the basal anatomy of the heart, its function, the origin and description of the electrocardiogram and a chapter on cardiac arrhythmias. It also includes a review of automatic detection of atrial fibrillation. The databases used in the practical part of the thesis are also described. The implementation of heart rhythm classification and automatic detection of the beginning and end of paroxysmal episodes is performed in MATLAB environment, the proposed algorithm is tested on the described databases and the results are evaluated.
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Detection of selected cardiac arrhythmias in ECG
Němečková, Karolína ; Ředina, Richard (referee) ; Ronzhina, Marina (advisor)
This thesis deals with classification of ECG records focusing on less classifiable arrhythmia (atrial flutter, atriventricular block I. and II. degree). In the theoretical part of the thesis deep learning used in classification of ECG records with a focus on the convolutional neural networks are described. The database of ECG records with a brief description of detected arrhythmias is further described. The practical part implements the proposed convolutional neural network in the Python environment. The evaluation of the arrhythmia detection quality was done using mainly the F1 score. The results were discussed at the end of the thesis.
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Detection of paroxysmal atrial fibrillation and atrial flutter
Krmela, Jan ; Němcová, Andrea (referee) ; Smíšek, Radovan (advisor)
This bachelor thesis deals with the problem of atrial fibrillation and flutter, the pathophysiology of these arrhythmias and their automatic detection. It includes a theoretical introduction necessary to understand the basal anatomy of the heart, its function, the origin and description of the electrocardiogram and a chapter on cardiac arrhythmias. It also includes a review of automatic detection of atrial fibrillation. The databases used in the practical part of the thesis are also described. The implementation of heart rhythm classification and automatic detection of the beginning and end of paroxysmal episodes is performed in MATLAB environment, the proposed algorithm is tested on the described databases and the results are evaluated.
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Detection of selected cardiac arrhythmias in ECG
Němečková, Karolína ; Ředina, Richard (referee) ; Ronzhina, Marina (advisor)
This thesis deals with classification of ECG records focusing on less classifiable arrhythmia (atrial flutter, atriventricular block I. and II. degree). In the theoretical part of the thesis deep learning used in classification of ECG records with a focus on the convolutional neural networks are described. The database of ECG records with a brief description of detected arrhythmias is further described. The practical part implements the proposed convolutional neural network in the Python environment. The evaluation of the arrhythmia detection quality was done using mainly the F1 score. The results were discussed at the end of the thesis.
|
|
|
Detection of poorly differentiated cardiac arrhythmias
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of atrial fibrilation, atrial flutter and sinus rhythm from ECG. Thesis also concentrate on the description of this arrhythmias and the learning algorithms used. In this thesis are implemented several classification approaches. For extraction of features is used convolution neural network and classification artifitial neural network. Selected 1D CNN method achived classification accuracy global F1 - score is 91 %. Moreover, the proposed CNN optimized with GA appears to be fast shallow network with better accuracy than the deep network. Created model are used for classification other type of arrhythmias too.
|
|
|
Atrial fibrillation model
Ředina, Richard ; Smíšek, Radovan (referee) ; Ronzhina, Marina (advisor)
The aim of this master thesis is to create a 3D electroanatomical model of a heart atria, which would be able to perform atrial fibrillation. To control the model, the differential equations of the FitzHugh-Nagumo model were chosen. These equations describe the change of voltage on the cell membrane. The equations have established parameters. The modification of them leads to changes in the behavior of the model. The simulations were performed in the COMSOL Multiphysics environment. In the first step, the simulations were performed on 2D models. Simulations of healthy heart, atrial flutter and atrial fibrillation were created. The acquired knowledge served as a basis for the creation of a 3D model on which atrial fibrillation was simulated on the basis of ectopic activity and reentry mechanism. Convincing results were obtained in accordance with the used literature. The advantages of computational modeling are its availability, zero ethical burden and the ability to simulate even rarer arrhythmias. The disadvantage of the procedure is the need to compromise between accuracy and computational complexity of simulations.
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