Spectral Analysis of Heart Rate Variability: Time Window Matters
Spectral Analysis of Heart Rate Variability: Time Window Matters
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DOI：
10.3389/fneur.2019.00545
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发表时间：
2019-05-29
影响因子：
3.4
通讯作者：
Ziemssen, Tjalf
中科院分区：
文献类型：
--
作者：
Li, Kai;Ruediger, Heinz;Ziemssen, Tjalf
心率变异性（HRV）的频谱分析是评估心血管自主功能的重要工具。快速傅里叶变换和基于自回归的频谱分析是HRV分析中最常用的两种方法，而三角回归谱（TRS）和小波变换等新技术也得到了发展。短期（几分钟的心电图）和长期（通常是1-24小时的心电图）HRV分析有不同的优缺点。本文综述了使用不同时间窗长度的光谱HRV研究的特点。短期HRV分析是一种方便的评估自主神经状态的方法，可以在几分钟内跟踪心脏自主神经功能的动态变化。长期HRV分析是评估自主神经功能的稳定工具，可以描述自主神经功能在数小时甚至更长的时间跨度内的变化，并且可以可靠地预测预后。选择合适的时间窗是利用谱HRV分析研究自主神经功能的关键。
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.