ArticleChaos, solitons, and fractals2020
Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Construction and Validation of an Automatic Segmentation Method for Respiratory Sound Time Labels.The clinical respiratory journal · 2025Article
- Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.Physical and engineering sciences in medicine · 2024Article
- Machine learning-based classification of valvular heart disease using cardiovascular risk factors.Scientific reports · 2024Article
- Body surface registration considering individual differences with non-rigid iterative closest point.International journal of computer assisted radiology and surgery · 2023Article
- Suppression of Clothing-Induced Acoustic Attenuation in Robotic Auscultation.Sensors (Basel, Switzerland) · 2023Article
- The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.International journal of environmental research and public health · 2022Article
- VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds.Computers in biology and medicine · 2022Article
- Analysis of agricultural exports based on deep learning and text mining.The Journal of supercomputing · 2022Article
- Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.Journal of biological physics · 2021Article
- A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19.Chaos, solitons, and fractals · 2021Article
- The Chaotic Behavior of the Spread of Infection During the COVID-19 Pandemic in the United States and Globally.IEEE access : practical innovations, open solutions · 2021Article
- Article
- Time series and fractal analyses of wheezing: a novel approach.Physical and engineering sciences in medicine · 2020Article
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Authors and funding
5 authors.
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Abstract
The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
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