|
|
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.
|
|
|
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.
|
|
|
Automatic detection of myocardial infarction in ECG
Nejedlý, Lukáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
This master’s thesis deals with the automatic detection of myocardial infarction in ECG. Semester work consists of two parts. The theoretical part provides a description of the electrical conduction system of the heart, spreading of electrical activity through the heart muscle, the methods of ECG scanning and the ECG curve. There are also mentioned the causes of myocardial ischemia and various methods of its detection. Another part is devoted to high-frequency ECG, analysis of HFQRS and clinical studies which describe the use of high-frequency ECG in diagnosis of myocardial infarction. In the practical part is proposed an algorithm using low-frequency components ECG and an algorithm using high-frequency components ECG for automatic detection of myocardial infarction. The proposed algorithms are implemented in programming environment MATLAB and tested on signals from the PTB database. The final part of the master‘s thesis is devoted to the comparison of the success of myocardial infarction by means of low frequency and high frequency components of ECG and comparison of achieved results with results from clinical studies.
|
|
|
Human activity classification
Müller, Jakub ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
This bachelor's thesis describes daily activity classification using accelerometric data. The first theoretical part summarizes the basics about daily activity and benefits that we get from monitoring it. In the next part of theory the principles of accelerometer inner workings are described. The last part of theory is dedicated to explaining the basics of neural networks and SVM. The aim of the practical part was to find a suitable dataset from a publicaly shared database, containing daily activity accelerometric data and also to collect our own data. Then performing classification using our own algorithm, optimizing it and finally evaluating the results.
|
|
|
Classification of free living data sensed with Faros
Šalamoun, Jan ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Topic of this master thesis is classification of free living data sensed with Faros. Faros is small compatible device which measure ECG and 3-axes accelerometric data. The first part of master thesis is find out how automatically measure free living activities by accelerometer and ECG. In next part was measured data of 8 activities from 10 probands. Automatic algorithms are made for this data in Matlab. This algorithms were used for this datasets and compare with manually recorded references. In the end of master thesis data were statistically evaluated.
|
|
|
Detection of true complete left bundle branch block
Opravilová, Kamila ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
The aim of this bachelor thesis is to get acquainted with the theory regarding electrophysiology of the heart and the pathology of Left Bundle Branch Block, LBBB for short. One of the goals is to make annotated database of QRS complexes for testing the LBBB algorithms. Next step will be to write and test these algorithms. Detection of LBBB is important, because it is one of the predictors of successfulness of resynchronization therapy. Conventional criteria for detection are not usable because of their low accuracy, that is why Straus' criteria had been made, those are way more accurate. This programme will abide these criteria. The overall evaluation of successfulness of this algorithm's detection is 100 % sensitivity and 69 % specifity. Therefore we can determine which patients do not suffer from LBBB without the risk of being wrong.
|
|
|
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.
|
|
|
Identification of Abnormal ECG Segments Using Multiple-Instance Learning
Šťávová, Karolína ; Smíšek, Radovan (referee) ; Hejč, Jakub (advisor)
Heart arrhythmias are a very common heart disease whose incidence is rising. This thesis is focused on the detection of premature ventricular contractions from 12-lead ECG records by means of deep learning. The location of these arrhythmias (key instances) in the record was found using a technique based on Multiple-Instance Learning. In the theoretical part of the thesis, basic electrophysiology of the heart and deep learning with a focus on the convolutional neural networks are described. Afterward, a program was created using the Python programming language, which contains a model based on the InceptionTime architecture, using which classification of the signals into the selected classes was performed. Grad-CAM was implemented to find locations of the key instances in the ECGs. The evaluation of the arrhythmia detection quality was done using the F1 score and the results were discussed at the end of the thesis.
|
|
|
Fetal ECG records analysis
Hláčiková, Michaela ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This thesis is focused on the analysis of fetal ECG records measured by indirect method from mother´s abdomen. The thesis consists of the theoretical part is focused on fetal, heart development and description of fetal ECG signal. This thesis also offers an overview of fECG signal processing methods used nowadays. The practical part of the thesis deals with the implementation of algorithms based on wavelet transformation and Least Mean Square LMS method in Matlab programming environment. The final part of the thesis consists of the analysis of achieved results.
|
|
|
Software Tools for the Analysis of Cardiac Depolarization in Patients with Heart Failure
Smíšek, Radovan ; Richter, Aleš (referee) ; Nováková, Marie (referee) ; Kolářová, Jana (advisor)
Heart failure is a disease in which the heart cannot adequately supply the body with oxygenated blood. Approximately 2% of people in the developed world have heart failure, which is increasing. Cardiac resynchronization therapy, which involves the implantation of a biventricular pacemaker, is used in patients with ventricular dyssynchrony to suppress or at least reduce the symptoms of heart failure. The problem is that 30–50% of patients do not benefit from performed resynchronization therapy. The correct patient selection and the pacemaker implantation and set-up method influence the success rate. The thesis proposes methodologies and software to facilitate the selection of patients suitable for cardiac resynchronization therapy, select the correct pacing lead placement, and optimize pacemaker settings. The developed software is based on standard parameters used in clinical practice (QRS duration, presence of left bundle branch block, classification of heart rhythm) and, in addition, on the analysis of ultra-high-frequency ECG, which provides more detailed information on cardiac activation than the standard parameters used. The developed software helps patients in 16 hospitals in the Czech Republic and abroad.
|
guest ::
RSS feed.