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Averaging of biological signals
Němeček, Tomáš ; Vítek, Martin (referee) ; Smital, Lukáš (advisor)
The main objectives of this thesis are to study theory of signal averaging, filtered residue method and methods of stretching/shrinking signal. It will also test the functionality of those methods. Thesis contains theoretical analysis, explanation of principles and testing of behaving of used methods.
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Methods of noise suppression for speech recognition systems
Moldříková, Zuzana ; Smital, Lukáš (referee) ; Odstrčilík, Jan (advisor)
This diploma thesis deals with methods of noise suppression for speech recognition systems. In theoretical part are discussed basic terms of this topic and also methods for noise suppression. These are spectral subtraction, Wiener filtering, RASTA, mapping of spectrogram or algorithms based on noise estimation. In second part types of noise are analyzed, there is proposal and implementation of spectral subtraction method of noise suppression for speech recognition system. Also extensive testing of spectral subtractive algorithms in comparison with Wiener filter is conducted. Assessment of this testing is done with defined metrics, successfulness of recognition, recognition system score and signal to noise ratio.
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Wavelet Wiener filter of ECG signals
Janů, Joshua ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
The thesis focuses on the use of wavelet wiener filtration to remove muscular interference from ECG signals. As part of it, a filter has been implemented in the MATLAB programming environment. The main part of the thesis deals with the optimization of numerical parameters of the proposed filter. The results of the filtration are compared with the results reported by other authors.
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Electrostimulation method for cardiomyocyte life extension
Čermáková, Adéla ; Smital, Lukáš (referee) ; Čmiel, Vratislav (advisor)
This work deals with the electrophysiology of cardiac cells, their electro-stimulation and design of stimulation device to extend their life. The first part is focused on the action potential in general, in muscle cells, followed by an analysis of the process and characteristics of the action potential in cardiomyocytes. The next section provides a procedure for isolating and process of changes that lead to cell death and of a method that the process would be eliminated. Part of this work is a program for processing and evaluation of pictures obtained during electro-stimulation process.
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Wavelet Wiener filter of ECG signals
Sedláčková, Eva ; Odstrčilík, Jan (referee) ; Smital, Lukáš (advisor)
The aim of this work is introduction with method of filtering the ECG signals using wavelet transformation and use of this method for filtering of signal disturbed with myopotencials. The work deals with general properties and with genesis of ECG signals and describes ECG curve. Next part of work is focused on wavelet transformation, types of wavelet transformation and different methods calculation thresholds and thresholding. Design part of work is focused on design Wiener filter for remove myopotencials from ECG signals and finding optimal parameters of this filter using optimization algorithm. For optimization is used simplex method. Discovered optimal parameters are assessed on CSE and MIT-BIH Arrhythmia database and compared with results of other authors.
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Detection of ischemia in ECG
Tichý, Pavel ; Smital, Lukáš (referee) ; Ronzhina, Marina (advisor)
This paper describes the manifestations of ischemia in the ECG signals and summarizes some methods allowing automatic detection of ischemia. Morphological features were then calculated from ECG signals available from UBMI and statistically evaluated to select features appropriate for further automatic classification. Multilayer feedforward neural network was used for classification of heart beats. The neural network was designed in Matlab. Classification performance up to 99.9% was obtained on available dataset.
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Delineation of ECG signals using methods combining
Zahradník, Radek ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
The aim of this work is to study and describe the principles and method of delineation of ECG signals. Learn and describe about method of cluster analysis. In this work was created and described three different methods of delineations of ECG signals. Created algorithms were tested on complete CSE database. With cluster analysis were combine created methods. The obtained results from realized methods and combined method were compared with others known methods. At the end of this work is evaluate efficiency of detection of combined method.
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Wavelet Filtering of ECG Signals
Handl, Marek ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The work deals with the wavelet transformation, focusing on wavelet transforms with discrete time (DTWT). The practical part is focused on the implementation of redundant packet DTWT and its use in the filtration of ECG signals. The main part of the work is to design wiener filter that uses redundant packet DTWT, designed to eliminate interference myopotentials of ECG signals. The actual solution is implemented in Matlab. Testing is performed on the library CSE using noise model myopotentials used to noising original signals. For optimum parameters designed filter is used the genetic algorithm (GA). The work is carried out comparing the proposed filter redundant packet DTWT a variant of redundant dyadic DTWT.
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Sleep scoring using EEG
Holdova, Kamila ; Smital, Lukáš (referee) ; Ronzhina, Marina (advisor)
This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.
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QRS detection using zero crossing counting
Hanus, Rostislav ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This master’s thesis deals with the detection of QRS complex detection using zero crossing counts. QRS detection is an important part of the analysis of ECG signal. From the point of determining the R wave detection is based on the other waves and intervals necessary for the diagnosis of heart. This method is very effective even for very noisy signals. Implementation of the method in Matlab, and the success of detection is tested on the CSE and MIT-BIH database. The optimization algorithm is an optional value for the detector.
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