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Detection of atrial fibrillation in short-term ECG
Ambrožová, Monika ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).
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ECG based atrial fibrillation detection
Prokopová, Ivona ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders characterized by ever-increasing prevalence and incidence in the Czech Republic and abroad. The incidence of atrial fibrillation is reported at 2-4 % of the population, but due to the often asymptomatic course, the real prevalence is even higher. The aim of this work is to design an algorithm for automatic detection of atrial fibrillation in the ECG record. In the practical part of this work, an algorithm for the detection of atrial fibrillation is proposed. For the detection itself, the k-nearest neighbor method, the support vector method and the multilayer neural network were used to classify ECG signals using features indicating the variability of RR intervals and the presence of the P wave in the ECG recordings. The best detection was achieved by a model using a multilayer neural network classification with two hidden layers. Results of success indicators: Sensitivity 91.23 %, Specificity 99.20 %, PPV 91.23 %, F-measure 91.23 % and Accuracy 98.53 %.
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Analysis of Atrial Fibrillation Heart Rate Dynamics
Tesařová, Tereza ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Bachalor thesis focuses on clinical tracking of heart rate abnormalities to specify the level of variability in diverse-lengthy ECG signals, with a direct way to analyze atrial fibrillation and accuracy increasing its detection of actual available methods. The principle of heart electrophysiology and also the impulse genesis is mentioned in a introductory part of work, including elementary classification of cardiac arrhytmias and their deflection from normal sinus rhythm. The physiological analysis of atrial fibrilation dynamic structure helps with the application of detection algorithms in a MATLAB program and sets an underlying basis for an appropriate discussion in a final statistical ROC analysis.
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Detection of atrial fibrillation in ECG
Húsková, Michaela ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
Aim of this thesis is description of problems of atrial fibrillation and methods that could be used for detection in the electrocardiogram. The introductory part of the theoretical analysis deals with the principle of electrophysiology of the heart and mainly the pathophysiology of atrial fibrillation. Additionally the work is focused on describing methods on automatic atrial fibrillation detection and capabilities of PhysioNet database. In the practical part methods are implemented in the MATLAB environment. After using the statistics to evaluate the quality of the parameters, the automatic classification of the data was performed by the method of The Nearest Neighbour. Finally, the accuracy of testing is presented.
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Detection of atrial fibrillation using intracardiac ECG
Kašparová, Michaela ; Novotná, Petra (referee) ; Ředina, Richard (advisor)
Atrial fibrillation is one of the most common heart rhythm disorders. It is caused by irreg- ular activation of the atria, which causes uncoordinated atrial contractions with a very high frequency, and therefore its early diagnosis is very important. The aim of this bachelor thesis is the automatic detection of atrial fibrillation in intracardiac recordings. The automatic detection allows finding the sections with atrial fibrillation and thus medi- ate a faster diagnosis. Testing of the developed algorithm was carried out on the available database of paediatric patients who underwent electrophysiological examination at the Children’s Hospital of the University Hospital Brno in the Czech Republic.
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Analysis of Atrial Fibrillation Heart Rate Dynamics
Tesařová, Tereza ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Bachalor thesis focuses on clinical tracking of heart rate abnormalities to specify the level of variability in diverse-lengthy ECG signals, with a direct way to analyze atrial fibrillation and accuracy increasing its detection of actual available methods. The principle of heart electrophysiology and also the impulse genesis is mentioned in a introductory part of work, including elementary classification of cardiac arrhytmias and their deflection from normal sinus rhythm. The physiological analysis of atrial fibrilation dynamic structure helps with the application of detection algorithms in a MATLAB program and sets an underlying basis for an appropriate discussion in a final statistical ROC analysis.
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|
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ECG based atrial fibrillation detection
Prokopová, Ivona ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders characterized by ever-increasing prevalence and incidence in the Czech Republic and abroad. The incidence of atrial fibrillation is reported at 2-4 % of the population, but due to the often asymptomatic course, the real prevalence is even higher. The aim of this work is to design an algorithm for automatic detection of atrial fibrillation in the ECG record. In the practical part of this work, an algorithm for the detection of atrial fibrillation is proposed. For the detection itself, the k-nearest neighbor method, the support vector method and the multilayer neural network were used to classify ECG signals using features indicating the variability of RR intervals and the presence of the P wave in the ECG recordings. The best detection was achieved by a model using a multilayer neural network classification with two hidden layers. Results of success indicators: Sensitivity 91.23 %, Specificity 99.20 %, PPV 91.23 %, F-measure 91.23 % and Accuracy 98.53 %.
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Detection of atrial fibrillation in short-term ECG
Ambrožová, Monika ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).
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Detection of atrial fibrillation in ECG
Húsková, Michaela ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
Aim of this thesis is description of problems of atrial fibrillation and methods that could be used for detection in the electrocardiogram. The introductory part of the theoretical analysis deals with the principle of electrophysiology of the heart and mainly the pathophysiology of atrial fibrillation. Additionally the work is focused on describing methods on automatic atrial fibrillation detection and capabilities of PhysioNet database. In the practical part methods are implemented in the MATLAB environment. After using the statistics to evaluate the quality of the parameters, the automatic classification of the data was performed by the method of The Nearest Neighbour. Finally, the accuracy of testing is presented.
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