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Conference papers

Physiological Signal Classification with Artificial Neural Networks

Nizar Kerkeni Mohamed Hedi Bédoui 1 Laurent Bougrain 2 Rafik Braham Mohamed Dogui
1 TIM - Laboratoire des Technologies de l'Information et de la Modélisation [Casablanca]
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A connectionist tool to help diagnose physiological signal, the Somatosensory Evoked Potentials (SEP), is presented. It is designed to be used in functional neurophysiological exploration. The tool should decide whether the signal is normal or pathological according to the inferior limbs SEP recording of an adult population. The obtained results (with a rate of success of 84%) allow us to think how to enlarge the application field of our tool to other evoked potentials and mainly to enlarge the data by the acquisition of SEP at different recording sites to locate the pathology.
Mots-clés : aide à la décision somesthesic evoked potentials decision making artificial neural networks physiological signals réseaux de neurones artificiels signaux physiologiques potentiels évoqués somesthésiques
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Conference papers
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https://hal.inria.fr/inria-00099795
Contributor : Publications Loria Connect in order to contact the contributor
Submitted on : Tuesday, September 26, 2006 - 9:41:19 AM
Last modification on : Friday, February 4, 2022 - 3:15:52 AM

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  • HAL Id : inria-00099795, version 1

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INRIA | CNRS | LORIA | UNIV-LORRAINE | INRIA2

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Nizar Kerkeni, Mohamed Hedi Bédoui, Laurent Bougrain, Rafik Braham, Mohamed Dogui. Physiological Signal Classification with Artificial Neural Networks. Computational Engineering in Systems Applications - CESA'2003, 2003, Lille, France, 4 p. ⟨inria-00099795⟩

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