Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering.
We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual 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 signals than classical nonparametric methodologies, and does not assume local stationarity 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 of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).
Aboy
Mateo
M
Department of Electronics Engineering Technology, Oregon Institute of Technology, Portland, OR 97201, USA. mateoaboy@ieee.org
Márquez
Oscar W
OW
McNames
James
J
Hornero
Roberto
R
Trong
Tran
T
Goldstein
Brahm
B
D003198:Computer Simulation; D006259:Craniocerebral Trauma / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007427:Intracranial Pressure; D008954:Models, Biological; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes