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Tytuł artykułu

Real-time estimation of the spectral parameters of Heart Rate Variability

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Spectral Heart Rate Variability (HRV) parameters, LF (low frequency) and HF (high frequency), have an important role in interpreting slower and faster heart rate modulations. An online analysis method of HRV spectral parameters based on the modified Hilbert–Huang Transform (HHT) is proposed in the paper. A number of novel methods have been put forward to meet the demand of causal pre-processing of interbeat time intervals (IBI) series prior to application of HHT. Also in the real-time implementation of the HHT which is the combination of the Empirical Mode Decomposition and Hilbert spectral analysis an original extrapolation method of intrinsic mode function related to LF and HF spectral parameters was applied. The proposed algorithm allows temporal estimation of HRV spectral parameters in real-time with delays being reduced up to 60% with respect to the Short Time Fourier Transform (STFT) analysis. Such reduction in analysis delay can have an important significance in a number of cardiologic invasive procedures, e.g. in cardio-resynchronisation therapy (CRT).
Twórcy
autor
  • Institute of Electronics, Lodz University of Technology, 211/215 Wolczanska Str., 90-924 Lodz, Poland
autor
  • Institute of Electronics, Lodz University of Technology, 211/215 Wolczanska Str., 90-924 Lodz, Poland
Bibliografia
  • [1] Acharya UR, Joseph KP, Kannathal N, Min LC, Jasjit SS. Heart rate variability: a review. Med Bio Eng Comput 2006;44:1031–51.
  • [2] Tarvainen MP, Niskanen JP, Lipponena JA, Ranta-ahoa PO, Karjalainen PA, Kubios HRV. Heart rate variability analysis software. Comput Methods Prog Biomed 2014;113:210–20.
  • [3] Kudrynski K, Strumillo P, Ruta J. Computer Software tool for heart rate variability (HRV), T-wave alternans (TWA) and heart rate turbulence (HRT) analysis from ECGs. Med Sci Monit 2011;17(9):MT63–71.
  • [4] Marple SL. Digital Spectral Analysis with Applications. Prentice Hall, Inc; 1987.
  • [5] Broersen PMT. Automatic spectral analysis with time series models. IEEE Trans Instrum Meas 2002;51(2):211–6.
  • [6] Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Eur Heart J 1996;17:354–81.
  • [7] Urbanek B, Ruta J, Kudryński K, Cygankiewicz I, Ptaszyński P, Kaczmarek K, et al. Influence of etiology of left ventricular dysfunction on heart rate variability parameters in short-term frequency domain analysis in patients qualified for cardiac resynchronization therapy. Pol J Cardiol 2011;13(1):5–9.
  • [8] Kudrynski K, Strumillo P. Real-time estimation of heart rate variability parameters from passband filtered interbeat interval series. Comput Cardiol 2011;38:297–300.
  • [9] Tkacz E. New diagnostic possibilities of heart rate variability (HRV) analysis;Habilitation dissertation, Gliwice [in Polish] 1996.
  • [10] Mateo J, Laguna P. Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Bio-Med Eng 2003;50(3):334–43.
  • [11] Akima H. A new method of interpolation and smooth curve fitting based on local procedures. J Assoc Comput Mach 1970;17(4):589–602.
  • [12] Clifford GD, Tarassenko L. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Trans Bio-Med Eng 2005;52(4):630–8.
  • [13] Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 1976;39:447–62.
  • [14] Marple SL. Computing the discrete-time analytic signal via FFT. IEEE Trans Signal Process 1999;47(9):2600–3.
  • [15] Tarvainen MP, Georgiadis S, Lipponen JA, Hakkarainen M, Karjalainen PA. Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm. Engineering in Medicine and Biology Society. EMBC Annual International Conference; 2009.
  • [16] Kudrynski K, Strumillo P. Estimation of instantaneous values of parameters of autoregressive heart rate variability model with the use of Kalman filtering. Biocybernetics and Biomedical Engineering Conference Proceedings; 2011 [in Polish].
  • [17] Li H, Kwong S, Yang L, Huang D, Xiao D. Hilbert–Huang transform for analysis of heart rate variability in cardiac health. IEEE/AC. Trans Comput Biol Bioinform 2011;8 (6):1557–67.
  • [18] Welch G, Bishop G. An introduction to the Kalman filter. Chapel Hill: UNC; 2006, TR 95-041.
  • [19] Sangkatumvong S, Coates TD, Wood JC, Meiselman HJ, Kato R, Detterich JA, et al. Time-varying analysis of autonomic control in response to spontaneous sighs in sickle cell anemia. Conf Proc IEEE Eng Med Biol Soc 2010;1626–9.
  • [20] Trnka P, Hofreiter M. The empirical mode decomposition in real-time. 18th International Conference on Process Control; 2011.
  • [21] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond 1998;A454:903–95.
  • [22] Oppenheim AV, Schafer RW. Discrete-time signal processing. 2nd ed. Prentice-Hall; 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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