The 2010 PhysioNet Challenge was to predict the last few seconds of a physiological waveform given its previous history and M-1 different concurrent physiological recordings. A robust approach was implemented by using a bank of adaptive filters to predict the desired channel. In all, M channels (the M-1 original signals, and 1 signal derived from the previous history of the target signal) were used to estimate the missing data. For each channel, a Gradient Adaptive Lattice Laguerre filter (GALL) was trained to estimate the desired channel. The GALL filter was chosen because of its fast convergence, stability, and ability to model a long response using relatively few parameters. The prediction of each of the channels (the output of each of the GALL filters) was then linearly combined using time-varying weights determined through a Kalman filter. This approach is extensible to recordings with any number of signals, other types of signals, and other problem domains. The code for the algorithm is freely available at PhysioNet under the GPL.
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