|
|
LabView and biosignal analysis
Kutílek, Miloš ; Harabiš, Vratislav (referee) ; Kolář, Radim (advisor)
This bachelor’s thesis deals with the analysis of biological signals in LabView environment. Mainly it focuses on the analysis of ECG signals. The thesis also describes the theory of electrocardiogram and occupies by several programs which consider various approaches to digital filtering of ECG signals, programs for detection R wave by different ways and simple cardiology monitor.
|
|
|
ECG analysis
Heczko, Marian ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
The topic of this master's thesis is the analysis of ECG signals using wavelet transform. In the introductory chapters there is a brief description of heart anatomy, the emergence and spread of potentials, which evocating activities of myocardium. There is an overview of techniques used for ECG signals analysis and explanation of ECG curve diagnostic importance. Work also containts an ECG signal analysis common procedure explanation and different approaches brief overview. The main part of this work is an application detecting significant intervals in the ECG signal, developed in Matlab. In several chapters the detection procedure is described in more details and gave reasons for chosen methods. In the last chapter there is a preview of several signals as a result of developed application, together with evaluation of the tests carried out at the CSE database. Detector sensitivity was quantified over 99,10%.
|
|
|
QRS detection based on wavelet transform
Zedníček, Vlastimil ; Ronzhina, Marina (referee) ; Smital, Lukáš (advisor)
This thesis deals with implementation of detector QRS complex with use of wavelet transform. The first part is focused on formation and possibility to measure cardiac activity. The other part of thesis we will familiarise with the different possibilities of detection QRS complex and we intimately focus on wavelet transform that will be used for a project of detection QRS complex. The practical part of thesis focuses on the project of detector in programming language Matlab and his different setting. This projected detector has been tested with CSE database. Achieved results of projected detector are evaluated with the results of others authors.
|
|
|
Dynamic time warping in biological signal processing
Brus, Lukáš ; Švrček, Martin (referee) ; Kolářová, Jana (advisor)
The Method of dynamic time warping is one of the modern scientific methods. It can be used for optimalization of analysis and classification of diagrams with biological signals. One part of the bachelor's thesis is to witness the generation of a biological process while studying the electrocardiogram and ECG classifications. The final part of the thesis is the discussion of the dynamic time warping and using these methods on a real data base of ECG signals from an experiment on an animal heart. This thesis mentions the function of DTW and also discusses a self-designed program for ECG diagnosing. This program, entitled ECG diagnose, is used for characterizing changes of the ECG signals during experiments performed on animal hearts.
|
|
|
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 %.
|
|
|
Deep-learning based localization of cardiac arrhythimas in ECG
Khaliullina, Sabina ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
The thesis deals with the localization and classification of atrial atrial fibrillation. In Python, a detection method using convolutional neural networks with multi-instance learning (MIL) and the method of local maxima for localization were implemented. Segments from two ECG leads were used. In the binary classification using the first subset and subsequent post processing, the F1 score reached 100 %, in the classification using the second subset 92 %. In the discussion and conclusion of the work, the success of classification and localization was evaluated, the achieved results were discussed and compared the with the results of other authors.
|
|
|
Design of the On Demand Pacemaker controlled by Microcontroller
Jarošová, Veronika ; Chmelař, Milan (referee) ; Sekora, Jiří (advisor)
The aim of this diploma work is the suggestion and realization of a cardiostimulator of „On Demand“ type operated by microcontroller. The work is structured on four thematic parts. For the correct suggestion of the whole system, firstly is necessary to understand correctly the cell’s electrophysiology and heart’s anatomy, inclusive the cardiology arrhytmia, which are adherent to cardiostimulators. The cardiostimulator is inhibited by the R-wave and is adaptive on a pulse rate of a source signal. The whole system is supplied by batteries and this supplying is taken into consideration. The network’s functionality is realized on the ECG simulator. There are suggested the suitable enhancements in discussion.
|
|
|
Classification of heart beats from multilead ECG using principal component analysis
Vlček, Milan ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
The resume of this master´s thesis is to introduce reader into principal component analysis (PCA), namely, the use of PCA for analysis of ECG. This method allows to reduce quantity of the data without loss of useful information. That is why PCA is widespread for preprocessing of the data for further classification, which this thesis also deals. Data available at the Department of Biomedical Engineering at the University of Technology in Brno were used in this work. All the methods were realized using Matlab.
|
|
|
Removing baseline wander in ECG with empirical mode decomposition
Procházka, Petr ; Kolářová, Jana (referee) ; Kubičková, Alena (advisor)
In this semestral thesis, realizations of chosen linear filters for baseline wander are described. These filters are then used on artificial ECG signals from CSE database with added baseline wander. These methods are compared and results are evaluated. After that, literature search of Empirical mode decomposition method is utilized. Realization of designed filters in MATLAB programming language are described, then results are evaluated with respect to filtration success.
|
|
|
Biological data averaging
Mlčoch, Marek ; Vítek, Martin (referee) ; Smital, Lukáš (advisor)
The thesis deals with the biological data averaging applied to a periodical and repetitive signal, specifically to an ECG signals. There were used signals from MIT-BIH Arrhythmia database and ÚBMI database. Averaging was realized with constant, floating and exponential Windows, where was used the method of addition of the filtered residue. This method is intended to capture the slow variations from the input to the output signal. The outcomes of these methods can be used as a basis for further work, or function as an example of principled methods. Methods and its outcomes were created in Matlab.
|
guest ::
