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Use of higher-order cumulants for ECG analysis
Maršánová, Lucie ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This work deals with using higher order cumulants for analysis ECG. In the first part of work is described principle of electrocardiography, followed by matemathical derivation of higher order cumulants, description of their properties and their use in current practice. In the next part of paper is described caltulation higher order statistic of ECG beats in Matlab programming environment. In the practical part of work are tested predicted properties. Distinctive properties are minimalization of amplitude and time shift and Gaussian noise. This properties of higher order cumulants enable lesser variance of beats in one class and easier clasification ECG. Calculation of cumulants from real ECG beats of various groups is then realized. Classification based on the original ECG cycles and cumulants is performed using artificial neural network. Results of these classification approaches are then compared and discussed.
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Analysis of experimental ECG
Maršánová, Lucie ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.
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Selected Items from a Firm´s Accounting Statements and their Analysis
Maršánová, Lucie Bc. ; Zmrhalová, Alena (advisor) ; Šubrt, Václav (referee)
Diplomová práce je rozdělena na teoretickou a prakticou část. Teoretická část práce je členěna na čtyři podkapitoly, které se postupně zabývají jednotlivými směry v účetnictví, účetními výkazy, oceňováním v účetnictví a poslední kapitola této části se zabývá pohledávkami a závazky. V praktické části jsou pak analyzovány pohledávky a závazky konkrétní společnosti. Dále je rozebrána problematika kurzových rozdílů a jejich vliv na výsledek hospodaření.
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