This paper presents a new technique of designing a Kalman filter for beat-to-beat arterial pressure estimation, based on observable/unobservable subspace decomposition. This method provides an approach to design a Kalman filter for ill-conditioned systems, whose overall state spaces are not observable but the variables of interest can be reconstructed from observable subspace, such as the arterial hemodynamic system used for continuous blood pressure monitoring. Thereafter, a low-order Kalman filter is designed for the observable subspace to estimate the variables, such as the peripheral blood pressure. Extensive experiments are conducted and the results are compared with the measurement from a FDA approved arterial tonometer to verify this method.

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