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Cough detection using a non-contact microphone: A nocturnal cough study

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  • Marina Eni
  • Valeria Mordoh
  • Yaniv Zigel

Abstract

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p

Suggested Citation

  • Marina Eni & Valeria Mordoh & Yaniv Zigel, 2022. "Cough detection using a non-contact microphone: A nocturnal cough study," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    • Handle: RePEc:plo:pone00:0262240
    DOI: 10.1371/journal.pone.0262240
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    1. Fallahpour, Saeid & Lakvan, Eisa Norouzian & Zadeh, Mohammad Hendijani, 2017. "Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 159-167.
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