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Cited by 69Phonocardiography and Auscultation TechniquesRespiratory and Cough-Related ResearchArtificial Intelligence in Healthcare

Cough Sound Analysis for Pneumonia and Asthma Classification in Pediatric Population

Amrulloh, Yusuf, Abeyratne, Udantha, Swarnkar, Vinayak, Triasih, Rina·2015·2015 6th International Conference on Intelligent Systems, Modelling and Simulation
DOI 10.1109/isms.2015.41
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Abstract

Pneumonia and asthma are the common diseases in pediatric population. The diseases share some similarities of symptoms that make them difficult to separate without the proper diagnostic tools. The majority of pneumonia cases occur in the third world countries wherein even the basic diagnostic tools (e.g.: x-ray) are extremely rare. In these countries, the WHO recommends using rapid breathing and chest in-drawing as approach to diagnose pneumonia in children with cough. As the results, many asthma patients were misdiagnosed as pneumonia and prescribed for unnecessary antibiotic treatment. In this study, we propose a cough sound analysis based method to differentiate pneumonia from asthma. Cough is the major symptom of pneumonia and asthma. Past studies showed the acoustic of cough sounds may carry important information related with the diseases. However, there were no attempts to use cough sounds to separate pneumonia and asthma in pediatric population. Our method extracted sound features such as Mel-frequency cepstral coefficients, non-Gaussianity score and Shannon entropy. The features were then used to develop artificial neural network classifiers. Tested using leave one out validation technique in eighteen subjects, our method achieved sensitivity, specificity and Kappa of 89%, 100%, and 0.89 respectively. The results show the potential of our method to be developed as a tool to differentiate pneumonia from asthma in remote areas.

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Authors
Amrulloh, Y., Abeyratne, U., Swarnkar, V., & Triasih, R.
Affiliations
  • The University of Queensland (AU)
  • Universitas Gadjah Mada (ID)
ORCID
Venue
2015 6th International Conference on Intelligent Systems, Modelling and Simulation
Publisher
IEEE
Published
2015-02
Pages
127-131
DOI
10.1109/isms.2015.41
OpenAlex
W1924943994
Cited by
69
References
22
Citations by year
2025: 7 · 2024: 9 · 2023: 9 · 2022: 14 · 2021: 11 · 2020: 5 · 2019: 5 · 2018: 3 · 2017: 5 · 2016: 1
FWCI
16.35
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Intelligent Systems, Modelling and Simulation (publishedVersion)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory and Cough-Related Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
cough sound analysis, pneumonia, asthma, pediatric respiratory disease, disease classification, acoustic features, Mel-frequency cepstral coefficients, Shannon entropy, neural network classifier, resource-limited settings, antibiotic misuse

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