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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2017: Bioinformatics and Biomedical Engineering pp 118–133Cite as

Diagnosis of Auditory Pathologies with Hidden Markov Models

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10208))

Abstract

Since about twenty years, the otoneurology functional exploration possesses auditory tool to analyze objectively the state of the nervous conduction of additive pathway. In this paper, we present a new classification approach based on the Hidden Markov Models (HMM) which used to design a Computer aided medical diagnostic (CAMD) tool that asserts auditory pathologies based on Brain-stem Evoked Response Auditory based biomedical test, which provides an effective measure of the integrity of the auditory pathway. Case study, experimental results and comparison with a conventional neural networks models have been reported and discussed.

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Acknowledgments

This research was supported by grants from “UNESCO for women in Science” and from “ReSMiQ” of Quebec.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, ÉTS, Montreal, Canada

    Lilia Lazli

  2. COFAMIC Laboratory, Department of Computer Science, UQAM, Montreal, Canada

    Lilia Lazli & Mounir Boukadoum

  3. Department of Computer Science, UBMA, Annaba, Algeria

    Lilia Lazli & Mohamed-Tayeb Laskri

  4. Department of Electrical Engineering and Computer Science, Concordia University, Montreal, Canada

    Otmane Aït-Mohamed

Authors
  1. Lilia Lazli
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  2. Mounir Boukadoum
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  3. Mohamed-Tayeb Laskri
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  4. Otmane Aït-Mohamed
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Corresponding author

Correspondence to Lilia Lazli .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. Universidad de Granada, Granada, Spain

    Francisco Ortuño

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Lazli, L., Boukadoum, M., Laskri, MT., Aït-Mohamed, O. (2017). Diagnosis of Auditory Pathologies with Hidden Markov Models. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-56148-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56147-9

  • Online ISBN: 978-3-319-56148-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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