| Summary: | 碩士 === 國立陽明大學 === 醫學工程研究所 === 95 === Recently, cardiovascular related diseases are the main healthy killer in both domestic and overseas people. Because heart rate variability can reflect individual autonomic nerves activity, by way of analysis of heart rate variability can quickly qualitative or quantitative understanding whether autonomic nerves adjustment is normal or not.
There’re many methods to analyze heart rate variability. So it’s important that confirm these methods if have its clinical significance. Once we confirmed this method have its clinical significance, we can further try to apply it to clinical diagnosis or prognosis. And it can let’s get more important information about physiological signals and more accurate prognosis information.
Detrended fluctuation analysis method is a fractal concept method that can be used to quantify hidden fractal properties from non-stationary physiological signal. In many methods of heart rate variability analysis, detrended fluctuation analysis is a method can overcome non-stationary physiological signal. DFA method also can detect fine change and provide powerful prognosis in physiological signals.
In this study, we will discuss the method of detrended fluctuation analysis if it has clinical significance. Our data all come from website database “Physionet”. And we got three different physiological dataset (normal sinus rhythm dataset, congestive heart failure dataset, and arrhythmia dataset) to analyze relative study. And we will use statistics to analyze these three groups and hope it can distinguish normal sinus rhythm dataset from congestive heart failure dataset and arrhythmia dataset respective.
We found DFA method’s parameters can effectively distinguish normal sinus rhythm group from congestive heart rate group and arrhythmia group respectively. It indicate that the DFA method have its clinical significance.
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