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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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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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Clustering of ECG cycles
Ředina, Richard ; Smíšek, Radovan (referee) ; Ronzhina, Marina (advisor)
The bachelor thesis explores the aplication of cluster analysis on diferent ECGs in order to create a reliable algorithm for detecting different QRS complexes. Algorithm comprises filtration, R-wave positions adjustment, model cycle creation and comparasion based on mean square error and correlation. Both, correlation and mean square error, become data for k-means clustering. The number of clusters is derived from silhouette values for diferent numbers of clusters.
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Early postoperative care of the patient with the left ventricular assist device HeartMate II
Malá, Irena ; Hocková, Jana (advisor) ; Slabý, Josef (referee)
Author's name: Bc. Irena Malá School: Charles university, Prague 1st Faculty of Medicine Institut of Theory and Practice of Nursing Vídeňská 800, 140 59 Prague 4 - Krč Program: Health Care Administration Title: Early postoperative care of the patient with the left ventricular assist device HeartMate II Diploma thesis supervisor: PhDr. Hocková Jana, PhD. Number of pages: 170 Number of attachments: 41 Year: 2013 Key words: early postoperative care, hypotermia, blood transfusion, fluid resuscitation, perioperative cardiovascular dysfunction, pharmacologic support, ventricular assist device HeartMateII, monitoration, device, cardiac arrhythmias, ventilation management, postoperative anticoagulation, glycemic kontrol, renal insufficiency, nutrition, nursing, complications, physiotherapy, psychological aspects The occurrence of the heart failure is similar to an epidemic with high mortality. This fact, together with stagnate or even decreasing number of suitable donors, led to a need of replacing the heart pump activity with an artificial one. Mechanical cardiac support systems are sophisticated devices that are able to support a certain period of time or completely replace the function of the heart as a pump. The indications implantation of mechanical cardiac support is significant symptomatic heart...
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Cluster analysis in biosignal processing
Kalous, Stanislav ; Archalous, Tomáš (referee) ; Kolářová, Jana (advisor)
This diploma thesis deals with cluster analysis for long-term electrocardiograms (ECG) clustering. The linear filtration is used for ECG preprocessing. The ECG sign segmenting in single heart cycles is based on the detection QRS complex and consequently to an application of dynamic time warping algorithms. To an application of all these mentioned processes and to results interpretation, a program called Cluster analysis has been created in the Matlab background. The results of this diploma thesis confirm that cluster analysis is able to distinguish cardiac arrhythmias which are typical with their shape distinctness of normal heart cycles.
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Anticoagulant therapy focusing on patients with atrial fibrillation
VANÍČKOVÁ, Martina
Thrombosis has recently become a widely discussed issue due to an increasing number of cases and problematic treatment. Once developed, thrombosis cannot be cured completely. Currently used anticoagulant treatment is limited to avoid complications such a hemorrhage or stroke. INR value checks (Quick tests) and monitoring covering at least 70% of the treatment period within the therapeutic range are necessary (2-3), but could be rather difficult to maintain in some patients. So-called new anticoagulants could prove themselves as an appropriate solution.
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