Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.

Ammini Renjini, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman

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Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Ammini RenjiniDepartment of Optoelectronics, University of Kerala, Trivandrum, Kerala, 695581, India.

Mohanachandran Nair Sindhu SwapnaLaboratory for Environmental and Life Sciences, University of Nova Gorica, Vipavska 13, Nova Gorica, SI-5000, Slovenia.

Sankaranarayana Iyer SankararamanDepartment of Optoelectronics, University of Kerala, Trivandrum, Kerala, 695581, India. drssraman@keralauniversity.ac.in.ORCID https://orcid.org/0000-0001-5374-6517

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing lung sounds and supporting earlier detection of various respiratory pathologies. The frequency spread and the component magnitudes are revealed from the analysis of eighty-five bronchial (BS) and pleural rub (PS) lung sounds employing the power spectral density (PSD) plot and wavelet scalogram. The low-frequency spread, and persistence of the high-intensity frequency components are visible in BS sounds emanating from the uniform cross-sectional area of the trachea. The frictional rub between the pleurae causes a higher frequency spread of low-intensity intermittent frequency components in PS signals. From the complex networks of BS and PS, the extracted graph features are - graph density ([Formula: see text], transitivity ([Formula: see text], degree centrality ([Formula: see text]), betweenness centrality ([Formula: see text], eigenvector centrality ([Formula: see text]), and graph entropy (E

Indexed as

BronchiSignal Processing, Computer-AssistedAlgorithmsAuscultationFemaleHumansMalePleuraRespiratory SoundsBronchial lung soundComplex networkNeural networkPleural rub lung sound

Identifiers

PMID38954378

DOI10.1051/epjconf/20146702047

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.