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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Acknowledgments
The authors would thank Dr. Chang Chunqi for his suggestions on this manuscript. This work has been supported by the general research fund of the Hong Kong research grant council.
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Zhang, Z.G., Tsui, K.M., Chan, S.C. et al. A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements. Med Biol Eng Comput 46, 789–797 (2008). https://doi.org/10.1007/s11517-008-0351-x
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DOI: https://doi.org/10.1007/s11517-008-0351-x