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10.1007/s13246-020-00937-5 http://scihub22266oqcxt.onion/10.1007/s13246-020-00937-5 33057901!7556586!33057901     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       Phys+Eng+Sci+Med 2020 ; 43 (4): 1339-1347 Nephropedia Template TP {{tp|p=33057901|t=2020. Time series and fractal analyses of wheezing: a novel approach |pdf=|usr=}}{{33057901}} gab.com Text Time series and fractal analyses of wheezing: a novel approach JO: Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33057901 #FOAvip Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis. Twit Text FOAVip Time series and fractal analyses of wheezing: a novel approach ????Phys Eng Sci Med http://www.kidney.de/pget.php?&tw=1&pmid=33057901 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33057901 #FOAvip English Wikipedia <ref>{{Cite pmid|33057901}}</ref> Time series and fractal analyses of wheezing: a novel approach #MMPMID33057901 Swapna MS; Renjini A; Raj V; Sreejyothi S; Sankararaman S Phys Eng Sci Med 2020[Dec]; 43 (4): 1339-1347 PMID33057901show ga Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis. |*Fractals[MESH] |Humans[MESH] |Principal Component Analysis[MESH] |Respiration[MESH] |Respiratory Sounds/*physiopathology[MESH] |Signal Processing, Computer-Assisted[MESH] |Time Factors[MESH]Time series and fractal analyses of wheezing: a novel approach (epmc).pdf DeepDyve Pubget Overpricing