ArticlePhysical and engineering sciences in medicine2020
Time series and fractal analyses of wheezing: a novel approach.
Article in Physical and engineering sciences in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 27 citations in OpenAlex.
- Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.Physical and engineering sciences in medicine · 2024Article
- Fractal complexity of daily physical activity and cognitive function in a midlife cohort.Scientific reports · 2023Article
- Fractal dimension based geographical clustering of COVID-19 time series data.Scientific reports · 2023Article
- Metaheuristic secured transmission in Telecare Medical Information System (TMIS) in the face of post-COVID-19.Journal of ambient intelligence and humanized computing · 2023Article
- Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.Journal of biological physics · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.
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