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Wavelet Filtering of ECG Signal
Slezák, Pavel ; Vítek, Martin (referee) ; Smital, Lukáš (advisor)
The thesis deals with possibilities of using wavelet transform in applications dealing with noise reduction, primarily in the field of ECG signals denoising. We assess the impact of the various filtration parameters setting as the thresholding wavelet coefficients method, thresholds level setting and the selection of decomposition and reconstruction filter banks.. Our results are compared with the results of linear filtering. The results of wavelet Wieners filtration with pilot estimation are described below. Mainly, we tested a combination of decomposition and reconstruction filter banks. All the filtration methods described here are tested on real ECG records with additive myopotential noise character and are implemented in the Matlab environment.
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Software ECG generator
Hendrych, Marek ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
The diploma thesis deals the cretion of the ECG signal and its potential morphology. A signal is generated using a program that is created in MATLAB. On the basis of these methods of describing the signal, was chosen method, based on the similarity of ECG with sinus respectively. triangular pattern. Generated by the program can draw the ECG signal by assignment of pulse rate, lenght of the signal, sampling rate and modifications of the waves and oscillations. One or more predefined noise can be added to the signal. Generated signal is possible to save to the format that supports program MATLAB.
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QRS Complex Detection Using Wavelet Transform
Loviška, David ; Čech, Petr (referee) ; Smital, Lukáš (advisor)
The aim of diploma thesis named “QRS detection using wavelet transform” is to simplify and accelerate the work of doctors. This can be achieved by using application for QRS detection, which can use one of four proposed algorithms. Application shows basic informations about inserted electrocardiogram. Part of this program is a graphical window with displayed record and with coloured marks on detected QRS complexes. By another algorythm are marks color-coded due to accurancy percentil of every detected complex. This program is designed for a several hours record from Holter ECG monitoring.
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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%.
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Classification of microsleep by means of analysis EEG signal
Ronzhina, Marina ; Smital, Lukáš (referee) ; Čmiel, Vratislav (advisor)
This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.
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