NDLI: Power spectral density estimation and tracking nonstationary pressure signals based on Kalman filtering

Content Provider	IEEE Xplore Digital Library
Author	Aboy, M. McNames, J. Marquez, O.W. Hornero, R. Thong, T. Goldstein, B.
Copyright Year	2004
Description	Author affiliation: BiomedicaI Signal Process. Laboratory, Electr. & Comput. Eng., Portland State Univ., OR, USA (Aboy, M.; McNames, J.)
Abstract	We describe an algorithm to estimate and track slow changes in power spectral density (PSD) of nonstationary pressure signals. The algorithm is based on a Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary pressure signals than classical nonparametric methodologies, and does not assume a piecewise stationary model of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation and tracking of short records of simulated pressure waveforms. This algorithm is intended for applications were the PSD must be estimated and tracked during short transient periods, possibly after clinical interventions.
Starting Page	156
Ending Page	159
File Size	450646
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1403115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Signal resolution Signal processing Frequency estimation Signal processing algorithms Signal analysis Filtering Biomedical engineering Autocorrelation Transient analysis intracranial pressure Kalman Filter spectral estimation linear models arterial blood pressure
Content Type	Text
Resource Type	Article

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