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

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 ( \( G)\) , transitivity ( \( T)\) , degree centrality ( \( {D}_{c}\) ), betweenness centrality ( \( {C}_{b})\) , eigenvector centrality ( \( {E}_{c}\) ), and graph entropy (E_n). The high values of \( G\) and \( T\) show a strong correlation between distinct segments of the BS signal originating from a consistent cross-sectional tracheal diameter and, hence, the generation of high-intense low-spread frequency components. An intermittent low-intense and a relatively greater frequency spread in PS signal appear as high \( {D}_{c}\) , \( {C}_{b}\) , \( {E}_{c}\) , and \( {E}_{n}\) values. With these complex network parameters as input attributes, the supervised machine learning techniques– discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)– classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).