MEMS piezoelectric resonant microphone array for lung sound classification

Journal of Micromechanics and Microengineering
PAPER • THE FOLLOWING ARTICLE ISOPEN ACCESS
MEMS piezoelectric resonant microphone array for lung sound classification
Hai Liu3,1, Matin Barekatain2,1, Akash Roy2,1, Song Liu1, Yunqi Cao1, Yongkui Tang1, Anton Shkel1 and Eun Sok Kim1

Published 9 March 2023 • © 2023 Author(s). Published by IOP Publishing Ltd
Journal of Micromechanics and Microengineering, Volume 33, Number 4
Citation Hai Liu et al 2023 J. Micromech. Microeng. 33 044003
DOI 10.1088/1361-6439/acbfc3
DownloadArticle PDF
Figures
References
Open science
Download PDF
878 Total downloads
Submit to this Journal
Turn on MathJax
Share this article

Share this content via email
Share on Facebook (opens new window)
Share on Twitter (opens new window)
Share on Mendeley (opens new window)
Article and author information
Abstract
This paper reports a highly sensitive piezoelectric microelectromechanical systems (MEMS) resonant microphone array (RMA) for detection and classification of wheezing in lung sounds. The RMA is composed of eight width-stepped cantilever resonant microphones with Mel-distributed resonance frequencies from 230 to 630 Hz, the main frequency range of wheezing. At the resonance frequencies, the unamplified sensitivities of the microphones in the RMA are between 86 and 265 mV Pa−1, while the signal-to-noise ratios (SNRs) for 1 Pa sound pressure are between 86.6 and 98.0 dBA. Over 200–650 Hz, the unamplified sensitivities are between 35 and 265 mV Pa−1, while the SNRs are between 79 and 98 dBA. Wheezing feature in lung sounds recorded by the RMA is more distinguishable than that recorded by a reference microphone with traditional flat sensitivity, and thus, the automatic classification accuracy of wheezing is higher with the lung sounds recorded by the RMA than with those by the reference microphone, when tested with deep learning algorithms on computer or with simple machine learning algorithms on low-power wireless chip set for wearable applications.