Time-Varying Analysis of Heart Rate Variability with Kalman Smoother Algorithm

A time-varying parametric spectrum estimation method for analyzing nonstationary heart rate variability signals is presented. In the method, the nonstationary signal is first modeled with time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The spectrum estimates for each time are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time-variation of low and high frequency components of heart rate variability can be examined separately

[1]  M. Akay,et al.  Short-term analysis of heart-rate variability of adapted wavelet transforms , 1997, IEEE Engineering in Medicine and Biology Magazine.

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  H. Nagaraja,et al.  Heart rate variability: origins, methods, and interpretive caveats. , 1997, Psychophysiology.

[4]  S. Barro,et al.  Time-frequency analysis of heart-rate variability , 1997, IEEE Engineering in Medicine and Biology Magazine.

[5]  S. Cerutti,et al.  Time-variant power spectrum analysis for the detection of transient episodes in HRV signal , 1993, IEEE Transactions on Biomedical Engineering.

[6]  H. Akaike.  Fitting autoregressive models for prediction , 1969 .

[7]  C. Striebel,et al.  On the maximum likelihood estimates for linear dynamic systems , 1965 .

[8]  N. Andersen,et al.  ON POWER ESTIMATION IN MAXIMUM ENTROPY SPECTRAL ANALYSIS , 1978 .

[9]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[10]  C. Marchesi,et al.  Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR) , 1996, IEEE Transactions on Biomedical Engineering.

[11]  H. Witte,et al.  Methods of dynamic spectral analysis by self-exciting autoregressive moving average models and their application to analysing biosignals , 2006, Medical and Biological Engineering and Computing.

[12]  H. Akaike.  A new look at the statistical model identification , 1974 .

[13]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[14]  Mika P. Tarvainen,et al.  Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS) , 2004, IEEE Transactions on Biomedical Engineering.

[15]  S Cerutti,et al.  Continuous monitoring of the sympatho-vagal balance through spectral analysis. , 1997, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[16]  Satoru Goto,et al.  On-line spectral estimation of nonstationary time series based on AR model parameter estimation and order selection with a forgetting factor , 1995, IEEE Trans. Signal Process..

[17]  Mika P. Tarvainen,et al.  An advanced detrending method with application to HRV analysis , 2002, IEEE Transactions on Biomedical Engineering.

[18]  S. Akselrod,et al.  Selective discrete Fourier transform algorithm for time-frequency analysis: method and application on simulated and cardiovascular signals , 1996, IEEE Transactions on Biomedical Engineering.

[19]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[20]  C. K. Yuen,et al.  Digital spectral analysis , 1979 .

[21]  S Cerutti,et al.  Automatic Decomposition of Wigner Distribution and its Application to Heart Rate Variability , 2004, Methods of Information in Medicine.

[22]  J. L. Melsa,et al.  Decision and Estimation Theory , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  V. Novak,et al.  Time/frequency mapping of the heart rate, blood pressure and respiratory signals , 1993, Medical and Biological Engineering and Computing.

[24]  T. Bohlin.  Analysis of EEG signals with changing spectra using a short-word Kalman estimator , 1977 .

[25]  Miki Haseyama,et al.  An ARMA order selection method with fuzzy reasoning , 2001, Signal Process..