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10.1007/s10867-021-09567-8 http://scihub22266oqcxt.onion/10.1007/s10867-021-09567-8 33905049!8076880!33905049     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biol+Phys 2021 ; 47 (2): 103-115 Nephropedia Template TP {{tp|p=33905049|t=2021. Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach |pdf=|usr=}}{{33905049}} gab.com Text Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach JO: J Biol Phys http://www.kidney.de/pget.php?&tw=1&pmid=33905049 #FOAvip The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system. Twit Text FOAVip Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach ????J Biol Phys http://www.kidney.de/pget.php?&tw=1&pmid=33905049 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33905049 #FOAvip English Wikipedia <ref>{{Cite pmid|33905049}}</ref> Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach #MMPMID33905049 Sreejyothi S; Renjini A; Raj V; Swapna MNS; Sankararaman SI J Biol Phys 2021[Jun]; 47 (2): 103-115 PMID33905049show ga The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system. |*Auscultation[MESH] |*Machine Learning[MESH] |*Signal Processing, Computer-Assisted[MESH] |COVID-19/physiopathology[MESH] |Fourier Analysis[MESH] |Humans[MESH] |Principal Component Analysis[MESH]Unwrapping the phase portrait features of adventitious crackle for aus(epmc).pdf DeepDyve Pubget Overpricing