Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

Background And Objective: Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice.

Methods: We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians.

Results: The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%.

Conclusions: The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition.