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Correlation dimension analysis for heart rate variability analysis
Ptáček, Ondřej ; Čmiel, Vratislav (referee) ; Janoušek, Oto (advisor)
Bachelor thesis deals with the analysis of heart rate variability (HRV). By focusing on the analysis of HRV in the time domain and mainly using correlation dimension. It is necessary to propose a methodology for evaluating changes in HRV data using these two techniques of analysis and the comparison of the two approaches. Subsequently create an application in MATLAB for analysis of HRV, which is able to evaluate the effect of changes in HRV caused by ischemia. In the last section it is necessary to discuss the results and the comparison of the two approaches to detect changes induced by ischemia.
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Correlation dimension analysis for heart rate variability analysis
Ptáček, Ondřej ; Čmiel, Vratislav (referee) ; Janoušek, Oto (advisor)
Bachelor thesis deals with the analysis of heart rate variability (HRV). By focusing on the analysis of HRV in the time domain and mainly using correlation dimension. It is necessary to propose a methodology for evaluating changes in HRV data using these two techniques of analysis and the comparison of the two approaches. Subsequently create an application in MATLAB for analysis of HRV, which is able to evaluate the effect of changes in HRV caused by ischemia. In the last section it is necessary to discuss the results and the comparison of the two approaches to detect changes induced by ischemia.
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Chaotic time-series prediction
Dědič, Martin ; Tichý, Vladimír (advisor) ; Smrčka, Pavel (referee)
This thesis focuses on possibility of chaotic (specially economic) time-series prediction. Chaotic time-series are unpredictable in long-term due to their high sensitivity on initial conditions. Nevertheless, their behavior should be more or less predictable in short-term. Goal of this thesis is to show, how much and if any prediction, is possible by non-linear prediction method, and try to reveal or to reject presence of chaotic behavior in them. Work is split into three chapters. Chapter One briefly introduces chosen important concepts and methods from this area. In addition, to describe some prediction methods, there are outlined which indicators and methods are possible to use in order to find possibilities and boundaries of this prediction. Chapter Two is focused on modifications of FracLab software, which is used for create this prediction. Last chapter is experimental. Besides the description of examined time-series and methods, it includes discussion of results.
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