School of Economics and Commerce and the Research Center of Financial Engineering, South China University of Technology, B10, Education Mega, Guangzhou 510006, China.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Nov-Dec;8(6):1557-67. doi: 10.1109/TCBB.2011.43.
This paper introduces a modified technique based on Hilbert-Huang transform (HHT) to improve the spectrum estimates of heart rate variability (HRV). In order to make the beat-to-beat (RR) interval be a function of time and produce an evenly sampled time series, we first adopt a preprocessing method to interpolate and resample the original RR interval. Then, the HHT, which is based on the empirical mode decomposition (EMD) approach to decompose the HRV signal into several monocomponent signals that become analytic signals by means of Hilbert transform, is proposed to extract the features of preprocessed time series and to characterize the dynamic behaviors of parasympathetic and sympathetic nervous system of heart. At last, the frequency behaviors of the Hilbert spectrum and Hilbert marginal spectrum (HMS) are studied to estimate the spectral traits of HRV signals. In this paper, two kinds of experiment data are used to compare our method with the conventional power spectral density (PSD) estimation. The analysis results of the simulated HRV series show that interpolation and resampling are basic requirements for HRV data processing, and HMS is superior to PSD estimation. On the other hand, in order to further prove the superiority of our approach, real HRV signals are collected from seven young health subjects under the condition that autonomic nervous system (ANS) is blocked by certain acute selective blocking drugs: atropine and metoprolol. The high-frequency power/total power ratio and low-frequency power/high-frequency power ratio indicate that compared with the Fourier spectrum based on principal dynamic mode, our method is more sensitive and effective to identify the low-frequency and high-frequency bands of HRV.
本文提出了一种基于希尔伯特-黄变换（HHT）的改进技术，以提高心率变异性（HRV）的谱估计。为了使逐拍（RR）间期成为时间的函数，并产生均匀采样的时间序列，我们首先采用预处理方法对原始 RR 间期进行插值和重采样。然后，提出了基于经验模态分解（EMD）方法的 HHT，将 HRV 信号分解为几个单分量信号，通过希尔伯特变换将其转换为解析信号，以提取预处理时间序列的特征，并描述心脏副交感和交感神经系统的动态行为。最后，研究了希尔伯特谱和希尔伯特边际谱（HMS）的频率行为，以估计 HRV 信号的谱特征。本文使用两种实验数据将我们的方法与传统的功率谱密度（PSD）估计进行比较。模拟 HRV 系列的分析结果表明，插值和重采样是 HRV 数据处理的基本要求，HMS 优于 PSD 估计。另一方面，为了进一步证明我们方法的优越性，我们从七名年轻健康受试者中采集了自主神经系统（ANS）被某些急性选择性阻断药物阻断时的真实 HRV 信号：阿托品和美托洛尔。高频功率/总功率比和低频功率/高频功率比表明，与基于主动态模式的傅里叶谱相比，我们的方法对识别 HRV 的低频和高频带更敏感和有效。
