Identification of Soft Tissue-Mimicking Materials and Application in the Characterization of Sensors for Lung Sounds

Abstract

Early diagnosis of pulmonary implications is fundamental for the treatment of several diseases, such as idiopathic pulmonary fibrosis, rheumatoid arthritis, connective tissue diseases and interstitial pneumonia secondary to COVID-19 among the many. Recent studies prove that a wide class of pulmonary diseases can be early detected by auscultation and suitably developed algorithms for the analysis of lung sounds. Indeed, the technical characteristics of sensors have an impact on the quality of the acquired lung sounds. The availability of a fair and quantitative approach to sensors’ comparison is a prerequisite for the development of new diagnostic tools. In this work the problem of a fair comparison between sensors for lung sounds is decoupled into two steps. The first part of this study is devoted to the identification of a synthetic material capable of mimicking the acoustic behavior of human soft tissues; this material is then adopted as a reference. In the second part, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">standard skin</i> is exploited to quantitatively compare several types of sensors in terms of noise floor and sensitivity. The proposed methodology leads to reproducible results and allows to consider sensors of different nature, e.g. laryngophone, electret microphone, digital MEMS microphone, mechanical phonendoscope and electronic phonendoscope. Finally, the experimental results are interpreted under the new perspective of equivalent sensitivity and some important guidelines for the design of new sensors are provided. These guidelines could represent the starting point for improving the devices for acquisition of lung sounds.