An acoustic system of sound acquisition and image generation for frequent and reliable lung function assessment

Lee, C. S., Li, M., Lou, Y., Abbasi, Q. H. and Imran, M. (2024) An acoustic system of sound acquisition and image generation for frequent and reliable lung function assessment. IEEE Sensors Journal, 24(3), pp. 3731-3747. (doi: 10.1109/jsen.2023.3344136)

Abstract

Lung sounds can be translated into acoustic imaging as an alternative to standard imaging to assess lung function frequently for improved therapy efficiency. This study proposes a comprehensive acoustic lung imaging system translated from acquired lung sounds for continual and reliable lung function assessment in response to the growing clinical interest in frequent lung function assessment. The proposed system comprises subsystems, such as data acquisition, signal processing, and imaging algorithm. This study demonstrated the design and implementation of a robust lung sound acquisition and imaging system using microelectromechanical microphones that reduce external noise contamination through redesigned hardware and dynamic signal processing. Regarding lung signal acquisition, the proposed system accomplished better root mean square error (RMSE) by around 0.15 and signal-to-noise ratio (SNR) by about 7 dB compared to commercial digital stethoscopes. RMSE and SNR reflect the accuracy in capturing desired signals and robustness-to-noise contamination and are used to quantitatively compare the system data acquisition to the commercially available acoustic and electronic devices in a noisy setting. The proposed system’s sensor position is neutral when representing lung signals, with a signal power loss ratio of around 5 dB compared to 10 dB from digital stethoscopes, in terms of the sensor area sensing sensitivity power spectrum mapping. The proposed system obtains about 7%–12% of more accurate detection of the actual nidus length than digital stethoscopes through imaging translated from acquired lung signals. Additionally, the detected airway obstruction results agree closely (91%) with airway remodeling studies.

• Item Type: Articles
• Additional Information: This work was supported and funded by the Singapore Economic Development Board (EDB).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Dr David and Lee, Chang Sheng and Abbasi, Professor Qammer and Imran, Professor Muhammad
• Authors: Lee, C. S., Li, M., Lou, Y., Abbasi, Q. H., and Imran, M.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 25 December 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Sensors Journal 24(3):3731-3747
• Publisher Policy: Reproduced in accordance with the publisher copyright policy