title: Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering.
abstract: 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).
authors.affiliation: Department of Electronics Engineering Technology, Oregon Institute of Technology, Portland, OR 97201, USA. mateoaboy@ieee.org
authors.forename: Mateo
authors.initials: M
authors.lastname: Aboy
authors.forename: Oscar W
authors.initials: OW
authors.lastname: Márquez
authors.forename: James
authors.initials: J
authors.lastname: McNames
authors.forename: Roberto
authors.initials: R
authors.lastname: Hornero
authors.forename: Tran
authors.initials: T
authors.lastname: Trong
authors.forename: Brahm
authors.initials: B
authors.lastname: Goldstein
mesh_terms: 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