A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements |
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Authors: | Z. G. Zhang K. M. Tsui S. C. Chan W. Y. Lau M. Aboy |
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Affiliation: | Department of Orthopaedics and Traumatology, and Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China, zgzhang@eee.hku.hk. |
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Abstract: | We present a novel parametric power spectral density (PSD) estimation algorithm for nonstationary signals based on a Kalman filter with variable number of measurements (KFVNM). The nonstationary signals under consideration are modeled as time-varying autoregressive (AR) processes. The proposed algorithm uses a block of measurements to estimate the time-varying AR coefficients and obtains high-resolution PSD estimates. The intersection of confidence intervals (ICI) rule is incorporated into the algorithm to generate a PSD with adaptive window size from a series of PSDs with different number of measurements. We report the results of a quantitative assessment study and show an illustrative example involving the application of the algorithm to intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI). |
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Keywords: | Cardiovascular pressure signal Kalman filter Power spectral density Time-varying autoregressive process Traumatic brain injury |
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