Prediction of arterial "burst" activity and transitions between chaotic attractors with a multilayer perceptron optimized by a new stopping criterion.

We have explored the potential of an artificial neural network to capture the dynamics of chaotic temporal fluctuations in arterial pressure and flow. Model generated signals that simulate this ubiquitous physiological phenomenon in both form and complexity were used to train a Multilayer Perceptron (MLP) after first locating the optimum time delay to unfold the attractor governing the dynamics. Prediction horizons were maximized with a new stopping criterion capable of continuously tracking the trajectories of the model system. Single-step predictions were consistently good throughout the study. Long-term predictions obtained by using the MLP as a signal generator were very successful when the number of hidden nodes was carefully chosen. Moreover, short- and long-term predictions could also be obtained even when the dynamics was nonstationary.