Hilbert-huang transform for analysis of heart rate variability in cardiac health

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

73 Scopus Citations
View graph of relations


  • Helong Li
  • Sam Kwong
  • Lihua Yang
  • Daren Huang
  • Dongping Xiao

Related Research Unit(s)


Original languageEnglish
Article number5728789
Pages (from-to)1557-1567
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
Publication statusPublished - 2011


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. © 2011 IEEE.

Research Area(s)

  • empirical mode decomposition (EMD), heart rate variability (HRV), Hilbert marginal spectrum (HMS), Hilbert-Huang transform (HHT), interpolation, resampling, spectrum estimation