| Title: | Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approach |
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| Authors: | MOHANACHANDRAN NAIR SINDHU SWAPNA,, SWAPNA (Author)
et al. |
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| Files: | This document has no files. This document may have a phisical copy in the library of the organization, check the status via COBISS.  |
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| Language: | English |
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| Work type: | Not categorized (r6) |
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| Tipology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica |
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| 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. Findings: The spectral analyses reveal fewer, low-intensity frequency
components along with its overtones in the intermittent and rapidly damping
RB signal. The complexity in the dynamics of airflow in BB and RB is investigated
through the fractal dimension, Hurst exponent, phase portrait, maximal
Lyapunov exponent, and sample entropy values. The greater value of entropy
for the RB signal provides an insight into the internal morphology of the airways
containing mucous and other obstructions. The Principal Component Analysis
(PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along
with Pattern Recognition Neural Network (PRNN) uses non-linear measures
for predicting BB and RB. Signal classification based on phase portrait
features evaluates the multidimensional aspects of signal intensities, whereas
that based on PSD features considers mere signal intensities. The principal
components in PCA cover about 86.5% of the overall variance of the data class,
successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100%
accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the
potential of non-linear measures and PSD features in classifying these signals
enabling its application to be extended for low-cost, non-invasive COVID-19
detection and real-time health monitoring. |
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| Keywords: | lung signal, fractal analysis, sample entropy, nonlinear timeseries, machine learning techniques |
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| Year of publishing: | 2022 |
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| Number of pages: | 1041-1051 |
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| Numbering: | 15, 21 |
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| COBISS_ID: | 113432067 |
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| URN: | URN:SI:UNG:REP:WWZ5LIAA |
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| DOI: | 10.17485/IJST/v15i21.627 |
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| License: | 
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International |
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| Views: | 552 |
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| Downloads: | 0 |
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