Power Spectral Density Estimation and Tracking of Nonstationary Pressure Signals based on Kalman Filtering

We describe an algorithm to estimate and track slow changes in power spectral density (PSD) of nonstationary pressure signals. The algorithm is based on a 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 pressure signals than classical nonparametric methodologies, and does not assume a piecewise stationary model 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 and tracking of short records of simulated pressure waveforms. This algorithm is intended for applications were the PSD must be estimated and tacked during short transient periods, possibly after clinical interventions. Keywords— Kalman Filter, spectral estimation, linear models, arterial blood pressure, intracranial pressure.