The exponential distribution applied to nonequidistantly sampled cardiovascular time series.

Beat-to-beat fluctuations in cardiovascular time series comprise different frequency components which can be employed to describe autonomic regulatory processes. The Exponential Distribution (ED) is presented here as a specific time-frequency distribution which has the potential to describe the time-related changes in the frequency content of these cardiovascular fluctuations. The ED has as advantage that it gives a good suppression of the cross terms, a characteristic feature of bilinear time-frequency distributions. An implementation to apply the ED to nonequidistantly sampled cardiovascular time series is provided. Applications of the ED to various clinical and experimental human cardiovascular time series show that the ED can be an important aid to describe and interpret time-varying frequency components of cardiovascular signals such as heart rate, interbeat interval, blood pressure, and respiration.

[1]  A Pedotti,et al.  Sequential spectral analysis of 24-hour blood pressure and pulse interval in humans. , 1990, Hypertension.

[2]  R. Cohen,et al.  Hemodynamic regulation: investigation by spectral analysis. , 1985, The American journal of physiology.

[3]  Boualem Boashash,et al.  Algorithms for Time-Frequency Signal Analysis , 1992 .

[4]  Boualem Boashash,et al.  Note on the use of the Wigner distribution for time-frequency signal analysis , 1988, IEEE Trans. Acoust. Speech Signal Process..

[5]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[6]  Boualem Boashash,et al.  An efficient real-time implementation of the Wigner-Ville distribution , 1987, IEEE Trans. Acoust. Speech Signal Process..

[7]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[8]  Boualem Boashash,et al.  A unified approach to the STFT, TFDs, and instantaneous frequency , 1992, IEEE Trans. Signal Process..

[9]  L J Mulder,et al.  Heart rate variability spectra based on non-equidistant sampling: the spectrum of counts and the instantaneous heart rate spectrum. , 1994, Medical engineering & physics.

[10]  Patrick Flandrin,et al.  Wigner-Ville spectral analysis of nonstationary processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[11]  O. Rompelman.  Parametric Spectral Analysis of Heart Rate Variability in Autonomie Neuropathy , 1984 .

[12]  P. Moleman,et al.  Cardiovascular Variability After Clonidine Challenge: Assessment of Dose‐Dependent Temporal Effects by Means of Spectral Analysis , 1993, Journal of cardiovascular pharmacology.

[13]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[14]  Mingui Sun,et al.  Efficient computation of the discrete pseudo-Wigner distribution , 1989, IEEE Trans. Acoust. Speech Signal Process..

[15]  A. V. van Roon,et al.  Spectral analysis of hemodynamics during infusions of epinephrine and norepinephrine in men. , 1994, Journal of applied physiology.

[16]  Françoise Peyrin,et al.  A unified definition for the discrete-time, discrete-frequency, and discrete-time/Frequency Wigner distributions , 1986, IEEE Trans. Acoust. Speech Signal Process..

[17]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[18]  L. Mulder.  Assessment of cardiovascular reactivity by means of spectral analysis , 1988 .

[19]  A. J. Man in 't Veld,et al.  Cardiovascular variability in major depressive disorder and effects of imipramine or mirtazapine (Org 3770). , 1996, Journal of clinical psychopharmacology.

[20]  J. Mayer,et al.  On the Quantum Correction for Thermodynamic Equilibrium , 1947 .