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Dynalets: A New Tool for Biological Signal Processing

  • Conference paper
Functional Statistics and Applications

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

The biological information coming from electro-physiologic signal sensors needs compression for an efficient medical use or for retaining only the pertinent explanatory information about the mechanisms at the origin of the recorded signal. When the signal is periodic in time and/or space, classical compression procedures like Fourier and wavelets transforms give good results concerning the compression rate, but provide in general no additional information about the interactions between the elements of the living system producing the studied signal. Here, we define a new transform called Dynalets based on Liénard differential equations susceptible to model the mechanism at the source of the signal and we propose to apply this new technique to real signals like ECG.

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Acknowledgements

We indebted to Campus France CMCU for supporting us with the grant PHC Maghreb SCIM.

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Authors and Affiliations

  1. AGIM CNRS FRE 3405, Faculty of Medicine, University J. Fourier of Grenoble, 38700, La Tronche, France

    Jacques Demongeot, Ali Hamie & Olivier Hansen

  2. AGIM CNRS FRE 3405, UPMF, UFR SHS, BP. 47, 38040, Grenoble Cedex 09, France

    Mustapha Rachdi

Authors
  1. Jacques Demongeot
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  2. Ali Hamie
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  3. Olivier Hansen
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  4. Mustapha Rachdi
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Corresponding author

Correspondence to Jacques Demongeot .

Editor information

Editors and Affiliations

  1. LMPA, Université du Littoral Côte d'Opale, Calais, France

    Elias Ould Saïd

  2. National School of Applied Sciences, Cadi Ayyad University, Marrakesh, Morocco

    Idir Ouassou

  3. Faculty of Science of Man and Society, Grenoble-Alpes University, Grenoble, France

    Mustapha Rachdi

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Demongeot, J., Hamie, A., Hansen, O., Rachdi, M. (2015). Dynalets: A New Tool for Biological Signal Processing . In: Ould Saïd, E., Ouassou, I., Rachdi, M. (eds) Functional Statistics and Applications. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-22476-3_9

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