A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements.
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).
Zhang
Z G
ZG
Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam Road, Hong Kong, China. zgzhang@eee.hku.hk
Tsui
K M
KM
Chan
S C
SC
Lau
W Y
WY
Aboy
M
M
D000465:Algorithms; D001794:Blood Pressure; D001930:Brain Injuries / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D007427:Intracranial Pressure; D008991:Monitoring, Physiologic / Q000379:methods; D012815:Signal Processing, Computer-Assisted