Department of Optoelectronics, University of Kerala, Trivandrum, Kerala, India, 695581.
Department of Optoelectronics, University of Kerala, Trivandrum, Kerala, India, 695581.
Comput Biol Med. 2022 Jun;145:105491. doi: 10.1016/j.compbiomed.2022.105491. Epub 2022 Apr 5.
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 (D) and eigenvector centrality (E). 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 D and E 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.
本文提出了一种听诊的图论方法，揭示了图特征在生物声学信号分类中的潜力。对 48 名健康人的 48 名健康人的生物声学信号（VE）和支气管（BR）呼吸声进行复杂网络分析，以了解呼吸期间的气流动力学。通过提取图特征（边数（E），图密度（D），传递性（T），度中心度（D）和特征向量中心度（E））的机器学习技术对 VE 和 BR 进行分类。BR 中的 E、D 和 T 值较高表明通过更宽的气管支气管道的时间相关气流导致持续的高强度低频。VE 中的频率扩展和高频，由于通过狭窄的节段性支气管和小叶的相关气流较少，因此 E、D 和 T 的值较低。D 和 E 的较低值证明了从光谱和其他图参数推断的合理性。该研究提出了一种在当前 COVID-19 情况下可用于远程听诊的方法。
