Abstract
A general framework is presented where time series properly embedded in a suitable embedding space with the time-delayed method are used by an artificial neural network to diagnose diseases and/or possible dynamical changes in the status of a subject, e.g. the progression of the disease itself. The first step includes the choice of an optimal delay time, that can be carried out with the aid of the auto-correlation or the mutual information functions. This is followed by the choice of the embedding dimension, carried out using the false nearest neighbours method. An auto-associative artificial neural network is then trained to recognize the dynamical trajectory in the pseudo state space. Finally, the error function of the ANN is used to evaluate outliers in the dynamics, that can be recognized as diseases or state transitions.
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