Physiological Signal Classification with Artificial Neural Networks

Nizar Kerkeni Mohamed Hedi Bédoui 1 Laurent Bougrain 2 Rafik Braham Mohamed Dogui
1 TIM - Laboratire des Technologies de l'Information et de la Modélisation
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
Type de document :
Communication dans un congrès
Computational Engineering in Systems Applications - CESA'2003, 2003, Lille, France, 4 p, 2003
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https://hal.inria.fr/inria-00099795
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 09:41:19
Dernière modification le : mardi 18 décembre 2018 - 10:56:06

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  • HAL Id : inria-00099795, version 1

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INRIA | LORIA | UNIV-LORRAINE

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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, 2003. 〈inria-00099795〉

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