Analysis of Vestibular-Ocular Reflex by Evolutionary Framework (original) (raw)

Abstract

In this paper the problem of analysis of eye movements using sinusoidal head rotation test is presented. The goal of the method is to discard automatically the effect of the fast phase-saccades and consequently calculate the response of vestibular system in the form of phase shift and amplitude. The comparison of threshold detection and inductive models trained on saccades is carried out. After saccades detection we are left with discontinuous signal segments. This paper presents an approach to align them to form a smooth signal with the same frequencies that were originally present in the source signal. The approach is based on a direct estimation of the signal component parameters using the evolutionary strategy with covariance matrix adaptation. The performance of evolutionary approach is compared to least-square multimodal sinus fit. The experimental evaluation on real-world signals revealed that threshold saccades detection with combination of the evolutionary strategy is robust, scalable and reliable method.

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References

  1. Gordon, J., Furman, J.M.R., Kamen, E.W.: System identification of the vestibulo-ocular reflex: application of the recursive least-squares algorithm. In: Proc. of the Fifteenth Annual Northeast Bioengineering Conference, pp. 199–200 (1989)
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Authors and Affiliations

  1. Czech Technical University in Prague, Czech Republic
    Daniel Novák, Aleš Pilný, Pavel Kordík, Štefan Holiga & Petr Pošík
  2. 2nd Medical Faculty, Charles University, Czech Republic
    R. Černý & Richard Brzezny

Authors

  1. Daniel Novák
  2. Aleš Pilný
  3. Pavel Kordík
  4. Štefan Holiga
  5. Petr Pošík
  6. R. Černý
  7. Richard Brzezny

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Novák, D. et al. (2008). Analysis of Vestibular-Ocular Reflex by Evolutionary Framework. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_47

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