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10.1016/j.chaos.2020.110246 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110246 32863618!7444955!32863618     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110246 Nephropedia Template TP {{tp|p=32863618|t=2020. Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation |pdf=|usr=}}{{32863618}} gab.com Text Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863618 #FOAvip 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. Twit Text FOAVip Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863618 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32863618 #FOAvip English Wikipedia <ref>{{Cite pmid|32863618}}</ref> Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation #MMPMID32863618 Raj V; Renjini A; Swapna MS; Sreejyothi S; Sankararaman S Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110246 PMID32863618show ga 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. äNonlinear time series and principal component analyses: Potential diag(epmc).pdf DeepDyve Pubget Overpricing