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Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm

Journal of Medical Systems volume 43, Article number: 255 (2019) Cite this article

Abstract

This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). It is concluded that combining lung sound based features with spirometry data can improve the accuracy of COPD diagnosis and hence the clinician’s performance in routine clinical practice. The proposed approach is of great significance in a clinical scenario wherein it can be used to assist clinicians for automated COPD diagnosis. A complete handheld medical system can be developed in the future incorporating lung sounds for COPD diagnosis using machine learning techniques.

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Author information

Affiliations

  1. Biomedical, NIT Raipur, G E Road, Raipur, 492010, India

    Nishi Shahnaj Haider & Bikesh Kumar Singh

  2. Department of Instrumentation and Control Engineering, NIT Tiruchirappall, Tamil Nadu, 620015, India

    R. Periyasamy

  3. Department of Pulmonary Medicine, AIIMS Raipur, Tatibandh, G E Road, Raipur, 492010, India

    Ajoy K. Behera

Authors
  1. Nishi Shahnaj Haider
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  2. Bikesh Kumar Singh
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Corresponding author

Correspondence to Bikesh Kumar Singh.

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Conflict of interest

Author Nishi Shahnaj Haider, declares that she has no conflict of interest. Author Bikesh Kumar Singh declares that he has no conflict of interest. Author R. Periyasamy, declares that he has no conflict of interest. Author Ajoy K. Behera, declares that he has no conflict of interest.

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Haider, N.S., Singh, B.K., Periyasamy, R. et al. Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm. J Med Syst 43, 255 (2019). https://doi.org/10.1007/s10916-019-1388-0

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  • DOI: https://doi.org/10.1007/s10916-019-1388-0

Keywords

  • Chronic obstructive pulmonary disease diagnosis
  • Lung sound
  • Feature extraction
  • Spirometry
  • Machine learning
  • Risk stratification