Spectral Analysis of Heart Rate Variability: Time Window Matters
Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressiv...
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doaj-feeb71926cb24f5aaccf0de06de2e61c2020-11-25T02:30:15ZengFrontiers Media S.A.Frontiers in Neurology1664-22952019-05-011010.3389/fneur.2019.00545426305Spectral Analysis of Heart Rate Variability: Time Window MattersKai Li0Kai Li1Heinz Rüdiger2Tjalf Ziemssen3Tjalf Ziemssen4Autonomic and Neuroendocrinological Lab, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, GermanyDepartment of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, ChinaAutonomic and Neuroendocrinological Lab, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, GermanyAutonomic and Neuroendocrinological Lab, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, GermanyDepartment of Neurology, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, GermanySpectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1–24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.https://www.frontiersin.org/article/10.3389/fneur.2019.00545/fulltrigonometric regressive spectral analysisfast fourier tranform (FFT)heart rate variabilitymultiple trigonometric regressive spectral analysislong-termshort-term |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kai Li Kai Li Heinz Rüdiger Tjalf Ziemssen Tjalf Ziemssen |
spellingShingle |
Kai Li Kai Li Heinz Rüdiger Tjalf Ziemssen Tjalf Ziemssen Spectral Analysis of Heart Rate Variability: Time Window Matters Frontiers in Neurology trigonometric regressive spectral analysis fast fourier tranform (FFT) heart rate variability multiple trigonometric regressive spectral analysis long-term short-term |
author_facet |
Kai Li Kai Li Heinz Rüdiger Tjalf Ziemssen Tjalf Ziemssen |
author_sort |
Kai Li |
title |
Spectral Analysis of Heart Rate Variability: Time Window Matters |
title_short |
Spectral Analysis of Heart Rate Variability: Time Window Matters |
title_full |
Spectral Analysis of Heart Rate Variability: Time Window Matters |
title_fullStr |
Spectral Analysis of Heart Rate Variability: Time Window Matters |
title_full_unstemmed |
Spectral Analysis of Heart Rate Variability: Time Window Matters |
title_sort |
spectral analysis of heart rate variability: time window matters |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neurology |
issn |
1664-2295 |
publishDate |
2019-05-01 |
description |
Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1–24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis. |
topic |
trigonometric regressive spectral analysis fast fourier tranform (FFT) heart rate variability multiple trigonometric regressive spectral analysis long-term short-term |
url |
https://www.frontiersin.org/article/10.3389/fneur.2019.00545/full |
work_keys_str_mv |
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