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Classification of Sounds Indicative of Respiratory Diseases

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Engineering Applications of Neural Networks (EANN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1000))

Abstract

This work presents a system achieving classification of respiratory sounds directly related to various diseases of the human respiratory system, such as asthma, COPD, and pneumonia. We designed a feature set based on wavelet packet analysis characterizing data coming from four sound classes, i.e. crack, wheeze, normal, crack+wheeze. Subsequently, the captured temporal patterns are learned by hidden Markov models (HMMs). Finally, classification is achieved via a directed acyclic graph scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution.

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Notes

  1. 1.

    https://bhichallenge.med.auth.gr/.

  2. 2.

    Freely available at http://torch.ch/.

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Acknowledgment

This research was funded by the ELKE TEI Crete funds related to the domestic project: Bioacoustic applications, number 80680.

Author information

Authors and Affiliations

  1. University of Milan, Milan, Italy

    Stavros Ntalampiras

  2. Technological Educational Institute of Crete, Rethymno, Greece

    Ilyas Potamitis

Authors
  1. Stavros Ntalampiras
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  2. Ilyas Potamitis
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Corresponding author

Correspondence to Ilyas Potamitis .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John Macintyre

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Oxford Brookes University, Oxford, UK

    Chrisina Jayne

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Ntalampiras, S., Potamitis, I. (2019). Classification of Sounds Indicative of Respiratory Diseases. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-20257-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20256-9

  • Online ISBN: 978-3-030-20257-6

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