| Title: | Bioacoustic signal analysis through complex network features |
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| Authors: | ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author)
ID VIMAL, RAJ, UNIVERSITY OF KERALA (Author)
ID S, Sankararaman, UNIVERSITY OF KERALA (Author) |
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| Files: |
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| Language: | English |
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| Work type: | Not categorized |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica
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| Abstract: | The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in
classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and
bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics
during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features
– the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality
(Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider
tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar,
appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral
and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in
the current scenario of COVID-19. |
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| Keywords: | Bioacoustic signal, Graph theory, Complex network, Lung auscultation |
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| Publication version: | Version of Record |
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| Year of publishing: | 2022 |
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| Number of pages: | 8 |
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| Numbering: | 6, 145 |
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| PID: | 20.500.12556/RUNG-7441 |
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| COBISS.SI-ID: | 113350147 |
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| DOI: | 10.1016/j.compbiomed.2022.105491 |
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| NUK URN: | URN:SI:UNG:REP:PERBKSNM |
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| Publication date in RUNG: | 30.06.2022 |
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| Views: | 2002 |
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| Downloads: | 0 |
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