Bioacoustic signal analysis through complex network features. (June 2022) - Raj, Vimal; Swapna, M.S.; Sankararaman, S.
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. Highlights: Unwraps the potential of graph features in distinguishing bioacuostic signals. Explains the respiratory airflow dynamics through graph features. Classifies signals, using MLTs, with greater accuracy, employing graph features. Revamps remote auscultation in the context of COVID-19 using graph theory.