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Detection of QRS complex using methods combining
Votoupal, Pavel ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This bachelor‘s thesis deals with QRS complex detection. There are described the basic information about ECG, QRS detection approaches, the methods of cluster analysis and description of detectors implemented in the MATLAB. These QRS detectors were tested and optimized on the CSE database. Finally, cluster analysis was performed for the combination of methods and the results were compared with the results of individual methods.
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Modern methods of QRS detection
Fajkus, Jiří ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This bachelor’s thesis deals with methods, which are used for processing of ECG signals, specifically for the detection of QRS complexes. The QRS detection is an essential part of every ECG analysis because it is a source of points with timing information, which are used for classification and measurement of other waves and intervals required for diagnostics. Some ways of QRS detection are described in this work and there is also a description of detector implemented in programming environment MATLAB, which is based on zero crossing counts. This QRS detector was then tested CSE database and signals from Holter examination.
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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.
|
|
|
Detection of QRS complex using methods combining
Votoupal, Pavel ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This bachelor‘s thesis deals with QRS complex detection. There are described the basic information about ECG, QRS detection approaches, the methods of cluster analysis and description of detectors implemented in the MATLAB. These QRS detectors were tested and optimized on the CSE database. Finally, cluster analysis was performed for the combination of methods and the results were compared with the results of individual methods.
|
|
|
Modern methods of QRS detection
Fajkus, Jiří ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This bachelor’s thesis deals with methods, which are used for processing of ECG signals, specifically for the detection of QRS complexes. The QRS detection is an essential part of every ECG analysis because it is a source of points with timing information, which are used for classification and measurement of other waves and intervals required for diagnostics. Some ways of QRS detection are described in this work and there is also a description of detector implemented in programming environment MATLAB, which is based on zero crossing counts. This QRS detector was then tested CSE database and signals from Holter examination.
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