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Application of neural networks for classification of T-wave alternations
Procházka, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.
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ECG noise generator
Mikuláš, Karol ; Kozumplík, Jiří (referee) ; Hrubeš, Jan (advisor)
Real ECG signal contains undesirable artifacts arising from its capture. Due to loss of diagnostically useful information in the noise signal and followed by the filtering the signal is important to know the characteristics, the most common symptoms and causes, to avoid or minimize the noise ECG signal. ECG noise generator is an educational program designed to serve the learning methods of filtering the noise signal. The user program can verify the effectiveness of the chosen method and the extent of deformation of the useful signal. The program includes basic types of artifacts, such as powerline interference, baseline wander, drift, impulse noise, myopotentials and change of baseline wander of ECG signal, which can be prepared noise signal from a database or other signal selected by the user.
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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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The use of genetic algorithm for edge detection in medical images
Slobodník, Michal ; Švrček, Martin (referee) ; Hrubeš, Jan (advisor)
This work deals with the possibilities of employing a genetic algorithm to edge detection. There is introduced a project which uses enhanced image divided into sub-regions, on which detection by genetic algorithm is applied. To achieving our goals are used individuals in two-dimensional bit arrays, for which are specially adjusted mutation and crossover operators. Cost minimization approach is used as fitness function. The project was created and tested in Matlab environment.
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Classification of ECG by artificial neural networks
Loviška, David ; Vítek, Martin (referee) ; Hrubeš, Jan (advisor)
The aim of project with name Classification ECG by artificial neural networks is simplify and speed up working a doctor. That reaches created program that the is capable simply and almost at once classify EKG signal using artificial neuronal nets. Created program will give to the doctor basic information about used electrocardiogram, as are time period and amplitude signal in single surveyed sections. Subsequently will program warn doctor about abnormalities from normal. Part of program is also graphic window with painted signal and on him in color points and partitions marked by program behind special. In next phase program alone classifies gained data and designating without doctor diagnose that doctor can evaluate and in case agreeable it sign and place for true diagnose patient. This program is also fit for data reading from bigger of the number of hours as far as days. It is concerned primarily Holter ECG monitoring.
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Neural networks and evolutionary algorithms
Vágnerová, Jitka ; Rychtárik, Milan (referee) ; Hrubeš, Jan (advisor)
Objective of this master's thesis is optimizing of neral network topology using some of evolutionary algorithms. The backpropagation neural network was optimized using genetic algorithms, evolutionary programming and evolutionary strategies. The text contains an application in the Matlab environment which applies these methods to simple tasks as pattern recognition and function prediction. Created graphs of fitness and error functions are included as a result of this thesis.
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QRS detection using zero crossing counts
Kašák, Pavel ; Hrubeš, Jan (referee) ; Vítek, Martin (advisor)
This thesis is focusing on problems of detection of QRS complex. It includes basic description of ECG signal. It acquaints with common methods of searching QRS complex. Main attention is paid to detection based on zero crossing counts, which is realized by MATLAB program. This algorithm is then tested on CSE database, optimised and its efficiency is evaluated.
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Detection of cardiac cells in microscopic image
Musikhina, Ksenia ; Hrubeš, Jan (referee) ; Rychtárik, Milan (advisor)
This work is devoted to problem of detection of cardiac cells in microscopic picture. All possible means of preprocessing and segmentation were considered with the aim to choose the most suitable method for further classification. Different methods of classification were be testing: method of objects attributes and classifier based on neural network. As a result was obtained the number of living and dead cardiac cells and percentage of them. The electivity of classification methods was calculated by sensitivity and specificity. The user’s interface was created for improvement of clearness classification in MATLAB environment.
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Suppression of powerline interference in ECG signals
Lacko, Michal ; Hrubeš, Jan (referee) ; Kozumplík, Jiří (advisor)
This project includes survey of various methods ECG signal filtering, to suppress of powerline interference. It is specialized especially on properties which affecting the quality of filtration. The main essence of project is evaluation of the proposed methods in terms of quality and the least possible distortion of the resulting ECG signal. The work is focused on linear, adaptive filtering and filtering using discrete wavelet trans-form. Signal processing and evaluation of descriptive parameters are transferred using the graphical interface GUI in the program Matlab 7.7.0 ( R2008b ).
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ECG noise generator
Kachlík, Miloš ; Vítek, Martin (referee) ; Hrubeš, Jan (advisor)
The aim of this work is to create the ECG signal noise generator in system MATLAB to verify the correct functioning of filtering methods. When the ECG signal is filtered, it can occur to its deformation. Differences before and after filtration are clearly visible, when it used a pure ECG signal. Well-defined interference is most appropriate for this purpose, because interference parameters are known and properties of filtering methods can be modified to achieve the maximum efficiency while the ECG signal shape distortion was minimal. Created program implements the basic types of ECG signal interferences, powerline interference, myopotentials, electrode artifacts and baseline wander.
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