Identification of soft tissue-mimicking materials and application in the characterization of sensors for lung sounds

Paolo La Torraca, Ludovico Ausiello, Giorgio Zucchi, Angelo Farina, Fabrizio Pancaldi

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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 standard skin 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.
Original languageEnglish
Pages (from-to)1012-1019
JournalIEEE Sensors Journal
Volume22
Issue number1
Early online date25 Nov 2021
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Acoustic sensor
  • human soft tissues
  • lung sounds
  • sensor bandwidth
  • sensor sensitivity

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