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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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PVC detection in ECG
Imramovská, Klára ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.
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PVC detection in ECG
Imramovská, Klára ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.
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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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