| Title: | Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features |
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| Authors: | Mohanachandran Nair Sindhu, 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: | Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis. |
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| Keywords: | Croup cough
Pertussis
Fractal dimension
Phase portrait
Sample entropy
Machine learning techniques |
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| Year of publishing: | 2021 |
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| Number of pages: | 214-222 |
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| Numbering: | 72, 4 |
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| COBISS_ID: | 113746947 |
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| URN: | URN:SI:UNG:REP:GHXYAVBV |
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| DOI: | https://doi.org/10.1016/j.cjph.2021.05.002 |
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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: | 373 |
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| Downloads: | 0 |
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