Physiological Signal Classification with Artificial Neural Networks

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.
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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
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Soumis le : mardi 26 septembre 2006 - 09:41:19
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099795, version 1

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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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