Abstract
Since about twenty years, the otoneurology functional exploration possesses experimental techniques to analyze objectively the state of the nervous conduction of auditive pathway. It conerns brainstem evoked response auditory. In this paper we present a new classification approach based on a hybrid neural network technique focusing this biomedical application for developing a diagnostic tool. We have used two models of artificial neural networks: Learning Vector Quantization and Radial Basis Function ones. In our approach, these two neural networks are used to achieve the classification in a serial multi-neural network configuration. Case study and experimental results have been reported and discussed.
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© 1999 Springer-Verlag Berlin Heidelberg
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Dujardin, AS., Amarger, V., Madani, K., Adam, O., Motsch, JF. (1999). Multi-neural network approach for classification of brainstem evoked response auditory. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100492
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DOI: https://doi.org/10.1007/BFb0100492
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