The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.

本文探讨了将分形，频谱和非线性时间序列分析应用于肺部听诊的合理性。通过快速傅立叶变换和小波分析的三十五个支气管（BB）和肺裂声（PC）声音信号不仅给出了频率分量的数量，性质和出现时间的详细信息，而且还把光投射到了嵌入的空气中呼吸过程中流动。分形维数，相像和样本熵有助于使BB中的气流动力学比PC散发更大的随机性，反持久性和复杂性。通过光谱特征提取的主成分分析潜力可分为BB，细裂纹和粗裂纹。与无监督的机器学习技术相比，基于相画像特征的监督分类被证明是更好的。由于考虑了时间序列信号的数据点之间的时间相关性，目前的工作阐明了相像特征作为分类的更好选择，从而提出了一种用于诊断肺病的新颖替代方法。该研究表明该技术在2019年冠状病毒疾病听诊中的可能应用会严重影响呼吸系统。