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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
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.
Detailed record
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.
Detailed record

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