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Chaotic map clustering algorithm for EEG analysis

Author

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  • Bellotti, R
  • De Carlo, F
  • Stramaglia, S

Abstract

The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

Suggested Citation

  • Bellotti, R & De Carlo, F & Stramaglia, S, 2004. "Chaotic map clustering algorithm for EEG analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 222-232.
    • Handle: RePEc:eee:phsmap:v:334:y:2004:i:1:p:222-232
    DOI: 10.1016/j.physa.2003.10.074
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    Cited by:

    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.

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