On-line Change-Point detection (for state space models) using multi-process Kalman filters

In this Paper we show how to use an on-line algorithm based on a multi-processKalman-filterextending ideas described in Whittaker and Frühwirth-Sehnatter (J. Whittaker, Frühwirth-Sehnatter, Appl. Stat. 43 (4) (1994)) to detect sequential Change-Points in noisy time series. We focus on types of Change-Points typically arising in biomedical Signals, i.e. jumps or drifts in nonstationary time series possibly corrupted by embedded outliers. The algorithm has been implemented in a program written in Matlab 5.0 and was tested using vital Parameters recorded during surgical procedures performed at the University Hospital of the Technical University of Munich, Klinikum Rechts der Isar, Munich.