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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
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Detection of ventricular extrasystoles using high-frequency components of ECG
Budíková, Barbora ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
Pathology of the ventricular extrasystole is commonly detected by comparing the width and other parameters of the QRS complex. This work represents recognition of extrasystoles using high-frequency ECG components, captured under special conditions, and rating of succes of this method. The detection is performed by algorithm in the Matlab programming environment and its output is an impulse propagation map and a decision, whether the complex is a ventricular extrasystole or a physiological complex QRS.
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Deep Convolutional Neural Network Model For Classification Of Atrial Fibrillation
Budíková, Barbora
Atrial fibrillation is a very common heart pathology, which is usually detected from electrocardiogram (ECG). This article presents recognition of atrial fibrillation in ECG using deep convolutional neural network. Data used for training the network includes physiological ECG, atrial fibrillation and nine other pathologies. The detection is performed by algorithm in Python language and is being assessed by accuracy and F1 measure.
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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
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Detection Of Ventricular Extrasystoles Using
High-Frequency Components Of Ecg
Budíková, Barbora
Pathology of the ventricular extrasystole is commonly detected by comparing the width and other parameters of the QRS complex. This article represents recognition of extrasystoles using high-frequency ECG components, captured under special conditions. The detection is performed by algorithm in the Matlab programming environment and its output is a map showing the passage of extrasystole through heart and decision whether complex is extrasystolic or physiologic.
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Detection of ventricular extrasystoles using high-frequency components of ECG
Budíková, Barbora ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
Pathology of the ventricular extrasystole is commonly detected by comparing the width and other parameters of the QRS complex. This work represents recognition of extrasystoles using high-frequency ECG components, captured under special conditions, and rating of succes of this method. The detection is performed by algorithm in the Matlab programming environment and its output is an impulse propagation map and a decision, whether the complex is a ventricular extrasystole or a physiological complex QRS.
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Czech-Canadian business cooperation with regards to cultural differences
Budíková, Barbora ; Müllerová, Františka (advisor) ; Karpová, Eva (referee)
The aim of the thesis is the analysis of business relations between the Czech Republic and Canada in terms of trade and cultural differences. The thesis is divided into four chapters. The first chapter presents Canada in terms of geography, demographic and describes the history of Canada. The second chapter focuses on the economic characteristics of the country, particularly macroeconomic indicators and international trade. The third chapter is devoted to Czech-Canadian business relations, the commodity structure of trade and industry perspective for Czech exports. The fourth chapter focuses on cultural diversity, knowledge of which is essential for business cooperation.
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