Evolutionary Maximum Entropy Spectral Estimation and HeartRate Variability Analysis

Authors:
S. I. Shah;L. F. Chaparro;A. El-Jaroudi;J. M. Furman
Affiliations:
Department of Electrical Engineering, 348 Benedum Hall, University of Pittsburgh, Pittsburgh, PA 15261;Department of Electrical Engineering, 348 Benedum Hall, University of Pittsburgh, Pittsburgh, PA 15261;Department of Electrical Engineering, 348 Benedum Hall, University of Pittsburgh, Pittsburgh, PA 15261;Department of Otolaryngology, 200 Lothrop Hall, University of Pittsburgh, Pittsburgh, PA 15261
Venue:
Multidimensional Systems and Signal Processing - Special issue on recent developments in time-frequency analysis
Year:
1998

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Abstract

Spectral analysishas been used extensively in heart rate variability (HRV) studies.The spectral content of HRV signals is useful in assessing thestatus of the autonomic nervous system. Although most of theHRV studies assume stationarity, the statistics of HRV signalschange with time due to transients caused by physiological phenomena.Therefore, the use of time-frequency analysis to estimate thetime-dependent spectrum of these non-stationary signals is ofgreat importance. Recently, the spectrogram, the Wigner distribution,and the evolutionary periodogram have been used to analyze HRVsignals. In this paper, we propose the application of the evolutionarymaximum entropy (EME) spectral analysis to HRV signals. The EMEspectral analysis is based on the maximum entropy method forstationary processes and the evolutionary spectral theory. Itconsists in finding an EME spectrum that matches the Fouriercoefficients of the evolutionary spectrum. The spectral parametersare efficiently calculated by means of the Levinson algorithm.The EME spectral estimator provides very good time-frequencyresolution, sidelobe reduction and parametric modeling of theevolutionary spectrum. With the help of real HRV signals we showthe superior performance of the EME over the earlier methods.