2022
DOI: 10.17485/ijst/v15i21.627
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Fractal and Time-series A nalyses based Rhonchi and Bronchial Auscultation: A Machine Learning Approach

Abstract: Objectives:The present work reports the study of 34 rhonchi (RB) and Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB and RB are studied using both Power Spectral Density (PSD) features and non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning models. Fi… Show more

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“…This proves that the proposed method is capable of producing high enough accuracy with the right selection of features. Another study combined the time series method with the fractal method to analyze several breath sounds [36], [37]. The use of several different features will directly add to the computational complexity.…”
Section: Hfd Measurement For Lung Sound Classificationmentioning
confidence: 99%
“…This proves that the proposed method is capable of producing high enough accuracy with the right selection of features. Another study combined the time series method with the fractal method to analyze several breath sounds [36], [37]. The use of several different features will directly add to the computational complexity.…”
Section: Hfd Measurement For Lung Sound Classificationmentioning
confidence: 99%