Raj Vimal, Renjini A, Swapna M S, Sreejyothi S, Sankararaman S
Department of Optoelectronics, University of Kerala, Trivandrum, Kerala, India- 695581.
Chaos Solitons Fractals. 2020 Nov;140:110246. doi: 10.1016/j.chaos.2020.110246. Epub 2020 Aug 24.
The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
在新型冠状病毒肺炎大流行爆发的背景下，新型数字听诊技术的发展变得极为重要。本研究报告了肺泡呼吸音（VB）和支气管呼吸音（BB）信号的频谱、非线性时间序列、分形和复杂性分析。分析使用了37个呼吸音信号。频谱分析通过功率谱密度图和小波尺度图揭示了VB和BB的特征。利用非线性时间序列和复杂性分析，从相图、分形维数、赫斯特指数和样本熵的角度研究了VB和BB期间呼吸道气流的动力学。通过最大李雅普诺夫指数揭示了BB相对于VB更高程度的混沌性。主成分分析通过从功率谱密度数据中提取特征，有助于对VB和BB声音信号进行分类。本研究中提出的方法简单、经济高效且灵敏，具有通过肺部听诊解决和诊断当前新型冠状病毒肺炎问题的深远潜力。
