Detrended fluctuation analysis of heart rate variability

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 qu...

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Main Authors: Shiang-Huan Hsieh, 謝祥煥
Other Authors: Woei-Chyn Chu
Format: Others
Language:zh-TW
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/69347793791535435180
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spelling ndltd-TW-095YM0055300152015-10-13T14:13:12Z http://ndltd.ncl.edu.tw/handle/69347793791535435180 Detrended fluctuation analysis of heart rate variability 心率變異度之去趨勢波動分析 Shiang-Huan Hsieh 謝祥煥 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. Woei-Chyn Chu 朱唯勤 2007 學位論文 ; thesis 52 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 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.
author2 Woei-Chyn Chu
author_facet Woei-Chyn Chu
Shiang-Huan Hsieh
謝祥煥
author Shiang-Huan Hsieh
謝祥煥
spellingShingle Shiang-Huan Hsieh
謝祥煥
Detrended fluctuation analysis of heart rate variability
author_sort Shiang-Huan Hsieh
title Detrended fluctuation analysis of heart rate variability
title_short Detrended fluctuation analysis of heart rate variability
title_full Detrended fluctuation analysis of heart rate variability
title_fullStr Detrended fluctuation analysis of heart rate variability
title_full_unstemmed Detrended fluctuation analysis of heart rate variability
title_sort detrended fluctuation analysis of heart rate variability
publishDate 2007
url http://ndltd.ncl.edu.tw/handle/69347793791535435180
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