In Journal of micromechanics and microengineering : structures, devices, and systems
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.
Liu Hai, Barekatain Matin, Roy Akash, Liu Song, Cao Yunqi, Tang Yongkui, Shkel Anton, Kim Eun Sok
2023-Apr-01
acoustic transducer, lung sounds classification, piezoelectric MEMS microphone, resonant microphone array, wearable health sensor
