MEMS piezoelectric resonant microphone array for lung sound classification

Abstract

This paper reports a highly sensitive piezoelectric MEMS resonant microphone array for detection and classification of wheezing in lung sounds. The resonant microphone array (RMA) is composed of 8 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, 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, 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.

Locating Nidi for High-Frequency Chest Wall Oscillation Smart Therapy via Acoustic Imaging of Lung Airways as a Spatial Network

“…Other MEMS have been used to record breathing patterns and respiratory rate, a feature that can also offer a thorough analysis of lung signals. Examples of these MEMS include MEMS accelerometers [51], [52], MEMS piezoelectric resonant microphones [53], and MEMS strain gauges [54]. As this study focuses on proposing a realistic 2D acoustic lung model incorporating spatial location to simulate airway obstruction and to design and optimize acoustic sensor array measurements quantitatively by applying generic acoustic sensor array design by considering only the sensor distribution, sensor sensitivity area, and the sensor number, readers who are interested in the fabrication of the various state-of-the-art MEMS can refer to [51], [53], [54] and the references therein for in-depth details.…”

An Acoustic System of Sound Acquisition and Image Generation for Frequent and Reliable Lung Function Assessment

“…In addition, the selection of the MEMS specification in this study was based on the CORSA -computerized respiratory sound analysis recommendations for sensor characteristics to detect human pulmonary sounds [8], [41]. Other MEMS, such as MEMS accelerometers [30], [40], MEMS piezoelectric resonant microphones [42], and strain gauges [43], have been utilized to capture breathing patterns and respiratory rate, a feature that is also currently unavailable in the digital stethoscope, to provide a comprehensive diagnosis with respect to lung signals. Readers who are interested in the precise diagnosis of respiratory disease can refer to [40], [42], [43] and the references therein for in-depth details on the fabrication of the various state-of-the-art MEMS.…”

Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine

“…Although digital stethoscopes can serve as a recording device, which can show useful acoustic signal information in a time-domain waveform, a computer algorithm must be developed to remove noise to retain critical data and convert the waveform into an image for an intuitive assessment so that the doctor or clinicians can easily identify obstructions in the airway to save time locating and assessing the obstructed area in the lungs. Leveraging on the size and its performance, recent work with various smart MEMS sensors, such as microphones [21] and accelerometers [22], has been adopted to capture acoustic signals. However, the patient's lung and environmental sounds had to be captured for the acoustic signal conditioning to improve the noise resolution and signal accuracy, which may pose privacy issues.…”