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Issue No.06 - November/December (2011 vol.8)
pp: 1557-1567
Sam Kwong , Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Helong Li , Res. Center of Financial Eng., South China Univ. of Technol., Guangzhou, China
Daren Huang , Sch. of Math. & Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China
Dongping Xiao , State Key Lab. of Power Transm. Equip. & Syst. Security & New Technol., Chongqing Univ., Chongqing, China
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.
medical signal processing, electrocardiography, Hilbert transforms, electrocardiogram, Hilbert-Huang transform, heart rate variability analysis, cardiac health, beat-to-beat interval, empirical mode decomposition approach, HRV signal, monocomponent signals, heart parasympathetic nervous system, heart sympathetic nervous system, conventional power spectral density estimation, young health subjects, autonomic nervous system, acute selective blocking drugs, atropine, metoprolol, Heart rate variability, Resonant frequency, Electrocardiography, Transforms, Frequency measurement, Hilbert space, Hilbert marginal spectrum (HMS)., Hilbert-Huang transform (HHT), heart rate variability (HRV), spectrum estimation, interpolation, resampling, empirical mode decomposition (EMD)
Sam Kwong, Helong Li, Daren Huang, Dongping Xiao, "Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 6, pp. 1557-1567, November/December 2011, doi:10.1109/TCBB.2011.43
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