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Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation

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  • Raj, Vimal
  • Renjini, A.
  • Swapna, M.S.
  • Sreejyothi, S.
  • Sankararaman, S.

Abstract

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306421
    DOI: 10.1016/j.chaos.2020.110246
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    References listed on IDEAS

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    1. 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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    Cited by:

    1. Juan D. Borrero & Jesus Mariscal, 2021. "Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series," Mathematics, MDPI, vol. 9(23), pages 1-18, November.
    2. Jahanshahi, Hadi & Munoz-Pacheco, Jesus M. & Bekiros, Stelios & Alotaibi, Naif D., 2021. "A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Nabin Sapkota & Atsuo Murata & Waldemar Karwowski & Mohammad Reza Davahli & Krzysztof Fiok & Awad M. Aljuaid & Tadeusz Marek & Tareq Ahram, 2022. "The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    4. 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).
    5. Ben-Loghfyry, Anouar & Charkaoui, Abderrahim, 2023. "Regularized Perona & Malik model involving Caputo time-fractional derivative with application to image denoising," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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