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Chaotic dynamics of respiratory sounds

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  • Ahlstrom, C.
  • Johansson, A.
  • Hult, P.
  • Ask, P.

Abstract

There is a growing interest in nonlinear analysis of respiratory sounds (RS), but little has been done to justify the use of nonlinear tools on such data. The aim of this paper is to investigate the stationarity, linearity and chaotic dynamics of recorded RS. Two independent data sets from 8+8 healthy subjects were recorded and investigated. The first set consisted of lung sounds (LS) recorded with an electronic stethoscope and the other of tracheal sounds (TS) recorded with a contact accelerometer. Recurrence plot analysis revealed that both LS and TS are quasistationary, with the parts corresponding to inspiratory and expiratory flow plateaus being stationary. Surrogate data tests could not provide statistically sufficient evidence regarding the nonlinearity of the data. The null hypothesis could not be rejected in 4 out of 32 LS cases and in 15 out of 32 TS cases. However, the Lyapunov spectra, the correlation dimension (D2) and the Kaplan–Yorke dimension (DKY) all indicate chaotic behavior. The Lyapunov analysis showed that the sum of the exponents was negative in all cases and that the largest exponent was found to be positive.

Suggested Citation

  • Ahlstrom, C. & Johansson, A. & Hult, P. & Ask, P., 2006. "Chaotic dynamics of respiratory sounds," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1054-1062.
    • Handle: RePEc:eee:chsofr:v:29:y:2006:i:5:p:1054-1062
    DOI: 10.1016/j.chaos.2005.08.197
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    References listed on IDEAS

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    1. Gnitecki, January & Moussavi, Zahra, 2005. "The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 26(4), pages 1065-1072.
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    Cited by:

    1. Jun, Ma & Wu-Yin, Jin & Yan-Long, Li, 2008. "Chaotic signal-induced dynamics of degenerate optical parametric oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 36(2), pages 494-499.
    2. 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).
    3. 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).
    4. Karkare, Siddharth & Saha, Goutam & Bhattacharya, Joydeep, 2009. "Investigating long-range correlation properties in EEG during complex cognitive tasks," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2067-2073.

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