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Development of a new diagnostic protocol using a neuro-dynamical tool

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  • Carniel, Roberto

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.

Suggested Citation

  • Carniel, Roberto, 2005. "Development of a new diagnostic protocol using a neuro-dynamical tool," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 349-352.
    • Handle: RePEc:eee:chsofr:v:24:y:2005:i:1:p:349-352
    DOI: 10.1016/j.chaos.2004.09.058
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    References listed on IDEAS

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    1. Carniel, Roberto & Pin, Enrico Del & Budai, Riccardo & Pascolo, Paolo, 2005. "Identifying timescales and possible precursors of the awake to asleep transition in EOG time series," Chaos, Solitons & Fractals, Elsevier, vol. 23(4), pages 1259-1266.
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    Cited by:

    1. Carniel, Roberto, 2008. "Diagnosing faults and illnesses with a quasi-time-invariant filter transfer function model," Chaos, Solitons & Fractals, Elsevier, vol. 35(1), pages 199-201.

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