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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
ABSTRACT
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.
INDEX TERMS
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)
CITATION
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
REFERENCES
[1] E. Bayly, “Spectral Analysis of Pulse Frequency Modulation in the Nervous Systems,” IEEE Trans. Biomedical Eng., vol. BME-15, no. 4, pp. 257-265, Oct. 1968.
[2] M. Malik, “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use,” Circulation: European Soc. Cardiology North Am. Soc. Pacing and Electrophysiology, vol. 93, pp. 1043-1065, 1996.
[3] G.G. Berntson, J.T. BiggerJr., D.L. Eckberg, P. Grossman, P.G. Kaufmann, M. Malik, H.N. Nagaraja, S.W. Porges, J.P. Saul, P.H. Stone, and M.W. Van Der Molen, “Heart Rate Variability: Origins, Methods, and Interpretive Caveats,” Psychophysiology, vol. 34, pp. 623-648, 1997.
[4] G.A. Myers, G.J. Martin, N.M. Magid, P.S. Barnett, J.W. Schaad, J.S. Weiss, M. Lesch, and D.H. Singer, “Power Spectral Analysis of Heart Rate Variability in Sudden Cardiac Death: Comparison to Other Methods,” IEEE Trans. Biomedical Eng., vol. BME-33, no. 12, pp. 1149-1156, Dec. 1986.
[5] U. Zwiener and D. Hoyer, “Deterministic Chaotic and Periodic Properties of Heart Rate and Arterial Pressure Fluctuations and Their Mediation in Piglets,” Cardiovascular Research, vol. 31, no. 3, pp. 455-465, 1996.
[6] F. Lombardi, “Chaos Theory, Heart Rate Variability, and Arrhythmic Mortality,” Circulation, vol. 101, pp. 8-10, 2000.
[7] C. Yang and T. Kuo, “Assessment of Cardiac Sympathetic Regulation by Respiratory-Related Arterial Pressure Variability in the Rat,” J. Physiology, vol. 515, pp. 887-896, 1999.
[8] A.M. Bianchi, L.T. Mainardi, C. Merloni, S. Chierchia, and S. Cerutti, “Continuous Monitoring of the Sympatho-Vagal Balance through Spectral Analysis,” IEEE Eng. in Medicine and Biology Magazine, vol. 16, no. 5, pp. 64-73, Sept./Oct. 1997.
[9] A. Zaza and F. Lombardi, “Autonomic Indexes Based on the Analysis of Heart Rate Variability: A View from the Sinus Node,” Cardiovascular Research, vol. 50, pp. 434-442, 2001.
[10] D.L. Eckberg, “Sympathovagal Balance: A Critical Appraisal,” Circulation, vol. 96, pp. 3224-3232, 1997.
[11] M.S. Houle and G.E. Billman, “Low-Frequency Component of Heart Rate Variability Spectrum: A Poor Marker of Sympathetic Activity,” Am. J. Physiology, vol. 276, pp. 215-223, 1999.
[12] E. Pyetan, O. Zoran, E. Toledo, and S. Akselrod, “A Theoretical Model for the Dependency of Heart Rate on Gradual Vagal Blockade by Atropine,” Computers in Cardiology, vol. 28, pp. 653-656, 2001.
[13] A. Porta, N. Montano, M. Pagani, A. Malliani, P. van de Borne, and V.K. Somers, “The Increase of Respiratory Sinus Arrhythmia during Low Dose Atropine Is Not Due to Changes of the Sinus Node Transfer Function or Baroreflex,” Computers in Cardiology, vol. 29, pp. 581-584, 2002.
[14] G. Sandrone, A. Mortara, D. Torzillo, M.T. La Rovere, A. Malliani, and F. Lombardi, “Effects of Beta Blockers (Atenolol or Metoprolol) on Heart Rate Variability After Acute Myocardial Infarction,” Am. J. Cardiology, vol. 74, pp. 340-345, 1994.
[15] P. Laguna, G.B. Moody, and R.G. Mark, “Power Spectral Density of Unevenly Sampled Data by Least Square Analysis Performance and Application to Heart Rate Signals,” IEEE Trans. Biomedical Eng., vol. 45, no. 6, pp. 698-715, June 1998.
[16] I.S. John and S. Ying, “Assessment of Chaotic Parameters in Non-Stationary Electrocardiograms by Use of Empirical Mode Decomposition,” Annals of Biomedical Eng., vol. 32, pp. 1348-1354, 2004.
[17] J.C. Echeverria and J.A. Crowe, “Application of Empirical Mode Decomposition to Heart Rate Variability Analysis,” Medical and Biological Eng. and Computing, vol. 39, pp. 471-479, 2001.
[18] R. Balocchi and D. Menicucci, “Deriving the Respiratory Sinus Arrhythmia from the Heartbeat Time Series Using Empirical Mode Decomposition,” Chaos, Solitons, and Fractals, vol. 20, pp. 171-177, 2004.
[19] Y. Zhong, H. Wang, K. Hwan Ju, K.-M. Jan, and K.H. Chon, “Nonlinear Analysis of the Separate Contributions of Autonomic Nervous Systems to Heart Rate Variability Using Principal Dynamic Modes,” IEEE Trans. Biomedical Eng., vol. 51, no. 2, pp. 255-262, Feb. 2004.
[20] W.G. Hawkins, “Fourier Transform Resampling: Theory and Application,” IEEE Trans. Nuclear Science, vol. 44, no. 4, pp. 1543-1551, Aug. 1997.
[21] J. Mateo and P. Laguna, “New Heart Rate Variability Time-Domain Signal Construction from the Bead Occurrence Time and the IPFM Model,” Computers in Cardiology, pp. 185-188, 1996, doi: 10.1109/CIC.1996.542504.
[22] J. Mateo and P. Laguna, “Improved Heart Rate Variability Signal Analysis from the Beat Occurrence Times According to the IPFM Model,” IEEE Trans. Biomedical Eng., vol. 47, no. 8, pp. 985-996, Aug. 2000.
[23] Q. Yuan, P.M. Thomas, and W. Rosaltind Picard, “Bayesian Spectrum Estimation of Unevenly Sampled Non-Stationary Data,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, pp. 1473-1476, 2002.
[24] G.L. Bretthorst, “Nonuniform Sampling: Bandwidth and Aliasing,” Proc. 19th Int'l Workshop Bayesian Inference and Maximum Entropy Methods in Science and Eng., vol. 567, pp. 1-28, 2001.
[25] M.E.D. Gomes, H.N. Guimaraes, A.L.P. Ribeiro, and L.A. Aguirre, “Does Preprocessing Change Nonlinear Measures of Heart Rate Variability?,” Computers in Biology and Medicine, vol. 32, pp. 481-494, 2002.
[26] G.D. Clifford and L. Tarassenko, “Quantifying Errors in Spectral Estimates of HRV Due to Beat Replacement and Resampling,” IEEE Trans. Biomedical Eng., vol. 52, no. 4, pp. 630-638, Apr. 2005.
[27] J. Jezewski, T. Kupka, and K. Horoba, “Extraction of Fetal Heart-Rate Signal as the Time Event Series from Evenly Sampled Data Acquired Using Doppler Ultrasound Technique,” IEEE Trans. Biomedical Eng., vol. 55, no. 2, pp. 805-810, Feb. 2008.
[28] S.R. Seydnejad and R.I. Kitney, “Real-Time Heart Rate Variability Extraction Using the Kaiserwidow,” IEEE Trans. Biomedical Eng., vol. 44, no. 10, pp. 990-1104, Oct. 1997.
[29] V. Dieter and B. Frank, “Wavelet Decomposition Analysis of Heart Rate Variability in Aerobic Athletes,” Autonomic Neuroscience, vol. 90, nos. 1-2, pp. 138-141, 20 July 2001.
[30] N.E. Huang et al., “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis,” Proc. Royal Soc. London Series A, vol. 454, pp. 903-995, 1998.
[31] E.P.S. Neto et al., “Assessment of Cardiovascular Autonomic Control by the Empirical Mode Decomposition,” Methods of Information in Medicine, vol. 43, no. 1, pp. 60-65, 2004.
[32] E.P.S. Neto et al., “Empirical Mode Decomposition to Assess Cardiovascular Autonomic Control in Rats,” Fundamental and Clinical Pharmacology, vol. 21, no. 5, pp. 481-496, 2007.
[33] M.R. Ortiz and E.R. Bojorges, “Analysis of High Frequency Fetal Heart Rate Variability Using Empirical Mode Decomposition,” Computers in Cardiology, vol. 32, pp. 675-678, Sept. 2005.
[34] K. Shafqat et al., “Empirical Mode Decomposition (EMD) Analysis of HRV Data from Locally Anesthetized Patients,” Proc. IEEE Ann. Int'l Conf. Eng. in Medicine and Biology Soc., pp. 2244-2247, 2009.
[35] E. Abdulhay et al., “Cardiogenic Oscillations Extraction in Inductive Plethysmography: Ensemble Empirical Mode Decomposition,” Proc. 31st IEEE Ann. Int'l Conf. Eng. in Medicine and Biology Soc., pp. 2240-2243, 2009.
[36] J.R. Yeh, S.Z. Fan, and J.S. Shieh, “Human Heart Beat Analysis Using a Modified Algorithm of Detrended Fluctuation Analysis Based on Empirical Mode Decomposition,” Medical Eng. and Physics, vol. 31, no. 1, pp. 92-100, 2009.
[37] L.J. Hadjileontiadis, “A Novel Technique for Denoising Explosive Lung Sounds Empirical Mode Decomposition and Fractal Dimension Filter,” IEEE Eng. in Medicine and Biology Magazine, vol. 26, no. 1, pp. 30-39, Jan./Feb. 2007.
[38] Y.F. Zhang;, Y.L. Gao, L. Wang, J.H. Chen, and X.L. Shi, “The Removal of Wall Components in Doppler Ultrasound Signals by Using the Empirical Mode Decomposition Algorithm,” IEEE Trans. Biomedical Eng., vol. 54, no. 9, pp. 1631-1642, Sept. 2007.
[39] B.V. Manuel, B.W. Weng, and E.B. Kenneth, “ECG Signal Denoising and Baseline Wander Correction Based on the Empirical Mode Decomposition,” Computers in Biology and Medicine, vol. 38, no. 1, pp. 1-13, 2008.
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