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Time series and mel frequency analyses of wet and dry cough signals: A neural net classification

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  • Renjini, Ammini
  • Swapna, Mohanachandran Nair Sindhu
  • Satheesh Kumar, Krishnan Nair
  • Sankararaman, Sankaranarayana Iyer

Abstract

The present study proposes a method for detecting and classifying wet (WC) and dry (DC) cough signals using spectral, cepstral, fractal, and nonlinear time-series analyses. Spectral analysis reveals multiple low-intensity frequency components in WC, leading to a broader spectral spread observed through periodogram, wavelet, and mel spectrograms. Cepstral analysis extracts important information from the cepstra of WC and DC, reducing dimensionality in the signal feature space. Complex phase portraits and higher values of nonlinear time-series parameters (maximal Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent) highlight the respiratory tract’s internal morphological changes due to complex airflow dynamics in WC. A supervised machine learning technique, neural network pattern recognition, utilizes these nonlinear parameters and cepstral coefficients as input predictors, achieving an impressive prediction accuracy of 99%. This research could significantly contribute to cough sound analysis, particularly in COVID-19 symptom identification, as the dry cough is one of the symptoms of the disease.

Suggested Citation

  • Renjini, Ammini & Swapna, Mohanachandran Nair Sindhu & Satheesh Kumar, Krishnan Nair & Sankararaman, Sankaranarayana Iyer, 2023. "Time series and mel frequency analyses of wet and dry cough signals: A neural net classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    • Handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123005940
    DOI: 10.1016/j.physa.2023.129039
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    References listed on IDEAS

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    1. Christopher M. Evans & Dorota S. Raclawska & Fani Ttofali & Deborah R. Liptzin & Ashley A. Fletcher & Daniel N. Harper & Maggie A. McGing & Melissa M. McElwee & Olatunji W. Williams & Elizabeth Sanche, 2015. "The polymeric mucin Muc5ac is required for allergic airway hyperreactivity," Nature Communications, Nature, vol. 6(1), pages 1-11, May.
    2. Raj, Vimal & Renjini, A. & Swapna, M.S. & Sreejyothi, S. & Sankararaman, S., 2020. "Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Ahlstrom, C. & Johansson, A. & Hult, P. & Ask, P., 2006. "Chaotic dynamics of respiratory sounds," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1054-1062.
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