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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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Use of HRV analysis for automatic detection of ischemia in animal isolated heart
Vykoupil, Pavel ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This paper deals with HRV analysis, creating segments for this analysis, calculating HRV parameters and their classification for automatic detection of ischemia. First part of the work is dedicated to theoretical describtion of heart anatomy, ECG reading, its processing and methods of HRV analysis. Next part of this work outline the principle of creating segments used for calculation of HRV parameters. Last part of the work indtroduces classification of said parameters with the help of multilayered neural networks and finding their best possible setup based on least classification error and computing time achieved. Calculation of HRV parameters and classification was realized using software Matlab.
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Delineation of experimental ECG data
Hejč, Jakub ; Janoušek, Oto (referee) ; Vítek, Martin (advisor)
This thesis deals with a proposition of an algorithm for QRS complex and typical ECG waves boundaries detection. It incorporates a literature research focused on heart electrophysiology and commonly used methods for ECG fiducial points detection and delineation. Out of the presented methods an algorithm based on a continuous wavelet transform is implemented. Detection and delineation algorithm is tested on CSE standard signal database towards references determined both manually and automatically. Obtained results are compared to other congenerous methods. The diploma thesis is further concerned with an algorithm modification for experimental electrocardiograms of isolated rabbit hearts. Recording specifics of these data are introduced. Additionally, based on time and frequency analysis, particular modifications of the algorithm are proposed and realized. Due to a large extent of records functionality is verified on randomly selected database samples. Efficiency of the modified algorithm is evaluated through manually annotated references.
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Classification of cardiac cycles
Lorenc, Patrik ; Škutková, Helena (referee) ; Vítek, Martin (advisor)
This work deals with the classification of cardiac cycles, which uses a method of dynamic time warping and cluster analysis. Method of dynamic time warping is among the elderly, but for its simplicity compared to others is still very much used, and also achieved good results in practice. Cluster analysis is used in many fields such as marketing or just for biological signals. The aim of this work is a general introduction to the ECG signal and the method and implementation of dynamic time warping algorithm. Subsequently, cluster analysis and finally the creation of the user interface for the algorithms.
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Delineation of experimental ECG data
Hanzelka, Adam ; Ronzhina, Marina (referee) ; Vítek, Martin (advisor)
This master's thesis deals with an analysis of principles of ECG signals detection and delineation. The theoretical part describes heart electrophysiology and electrocardiography basics. Next, the most important QRS detection and ECG delineation algorithms are introduced. Especially the wavelet transform methods are described. In the practical part proper delineation algoriythm was realized. It was tested on the standard CSE database, then it was modified on data of isolated rabbit heartsand the results are published in the conclusion.
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ECG classification using methods of HRV analysis
Caha, Martin ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This paper deals with the classification of ECG measured from isolated rabbit hearts during the experiment with repeated ischemia. Classification features were calculated using the methods of heart rate variability analysis. The results were statistically evaluated. Heart rate variability parameters were calculated using Kubios HRV, other calculations were performed in MATLAB. Artificial neural network was created to classify the analyzed parameters to specific groups.
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Time Varying Filters for ECG Signals
Peterek, Jan ; Vítek, Martin (referee) ; Kozumplík, Jiří (advisor)
The aim of this master’s thesis is to create a multiband stop derived from Lynn filters for suppressing mains hum and baseline variation (drift). The first part of the thesis is focused on brief theoretical introduction to the distortion types affecting ECG signal and twelve lead connection. The following practical part describes free realizations of ECG filter and ECG signal filtration. The filter has been tested both on distorted and on non-distorted signal. Finally filters’ error rate was computed from CSE database signals.
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Delineation of ECG signals using leads transformation
Ondroušek, Lukáš ; Ronzhina, Marina (referee) ; Vítek, Martin (advisor)
The goal of this work is to study the principles of delineation of ECG signals, wavelet transformation and transformation approaches to increase the number of available leads. Consequently, the knowledge was used to create delineation algorithm in Matlab. The algorithm was tested on complete CSE database. The obtained results were compared with the criteria which are set for the CSE database. In this work were realized six transformation approaches to increase the number of available leads. All of them were analyzed by delineation algorithm. In the work was examined, whether the transformation increase the efficiency of detection.
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The use of genomic signal compression for classification and identification of organisms
Sedlář, Karel ; Vítek, Martin (referee) ; Škutková, Helena (advisor)
Modern classification of organisms is performed on molecular data. These methods rely on multiple alignment of sequences of characters which make them computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, the novel algorithm based on conversion of the whole genome sequences to cumulative phase signals is presented. Dyadic wavelet transform is used for lossy compression of signals by redundant frequency bands elimination. Signal classification is then performed as a cluster analysis using Euclidian metrics where multiple alignment is replaced by dynamic time warping.
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