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Related Experiment Video

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Using Micro-Electro-Mechanical Systems MEMS to Develop Diagnostic Tools
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MEMS piezoelectric resonant microphone array for lung sound classification.

Hai Liu1, Matin Barekatain1, Akash Roy1

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States of America.

Journal of Micromechanics and Microengineering : Structures, Devices, and Systems
|March 13, 2023
PubMed
Summary

A new resonant microphone array (RMA) using piezoelectric microelectromechanical systems (MEMS) enhances lung sound analysis. This sensitive device improves wheezing detection and classification accuracy for both computer and wearable applications.

Keywords:
acoustic transducerlung sounds classificationpiezoelectric MEMS microphoneresonant microphone arraywearable health sensor

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Area of Science:

  • Engineering
  • Biomedical Engineering
  • Acoustics

Background:

  • Lung sound auscultation is crucial for diagnosing respiratory conditions.
  • Traditional microphones may not optimally capture subtle wheezing frequencies.
  • Microelectromechanical systems (MEMS) offer miniaturization and high sensitivity for acoustic sensing.

Purpose of the Study:

  • To develop and evaluate a highly sensitive piezoelectric MEMS resonant microphone array (RMA) for improved detection and classification of wheezing in lung sounds.
  • To assess the RMA's performance compared to a traditional reference microphone.

Main Methods:

  • Fabrication of an 8-microphone RMA with width-stepped cantilever resonators.
  • Resonance frequencies were Mel-distributed between 230-630 Hz, covering the primary wheezing range.
  • Characterization of microphone sensitivity and signal-to-noise ratios (SNRs) across relevant frequencies.
  • Testing with deep learning (computer) and simple machine learning (wearable chipsets) algorithms.

Main Results:

  • Microphone sensitivities ranged from 86 to 265 mV/Pa at resonance and 35 to 265 mV/Pa over 200-650 Hz.
  • SNRs were high, between 86.6-98.0 dBA at resonance and 79-98 dBA over 200-650 Hz.
  • Wheezing features were more distinguishable with the RMA, leading to higher automatic classification accuracy.

Conclusions:

  • The piezoelectric MEMS RMA demonstrates superior performance for capturing and analyzing wheezing sounds.
  • The enhanced signal distinguishability facilitates more accurate automated wheezing classification.
  • This technology holds promise for advanced respiratory monitoring in both clinical and wearable settings.