Aiming at the low sensitivity of traditional stethoscope and the application value of electronic stethoscope in the early diagnosis of cardiovascular diseases, in this paper,a bionic electronic stethoscopes based on MEMS technology is designed. The feasibility of micro-structure of MEMS electronic sensor is verified by theoretical analysis and finite element simulation. And the sensor acquisition circuit and the PC upper machine are designed for the real-time acquisition of heart sound signal. The focuses of this paper is to design and optimize the micro-structure of the sensor, and improve of the working bandwidth and the signal-to-noise ratio. The final test results show that the sensitivity of the sensor is −189.5dB (@500Hz), the working bandwidth is 20-600Hz, and the SNR is 25.62dB, which is 2.59dB higher than the similar products of 3M Littmann 3200 electronic stethoscope.

Heart sound auscultation is the most important technique physicians use to examine cardiovascular diseases. Doctors use their clinical experience combined with listening to the patient’s first heart sound (S1), second heart sound (S2), and heart murmurs during auscultation to conduct preliminary screening for cardiovascular diseases. However, doctors with insufficient clinical experience in auscultating heart sounds (PCG) find it challenging to accurately identify the characteristic signals in the aforementioned heart sound signals. The onset of the first heart sound in heart sound signals corresponds to the peak of the R wave in the electrocardiogram (ECG) signal, and the onset of the second heart sound corresponds to the T wave. Therefore, by combining the timing relationship between the two, doctors can effectively identify the characteristics of heart sound signals, thus improving the accuracy of screening for cardiovascular diseases. this paper presents the design of an auscultation system capable of simultaneously measuring both PCG signals and ECG signals while adhering to medical electrical safety standards and medical electromagnetic compatibility standards. The designed synchronous auscultation system successfully passed rigorous testing for compliance with both medical electrical safety and medical electromagnetic compatibility standards. This testing validated the system’s security and stability. The system noise in the ECG channel was less than 30 μV and the frequency response ranged from 0.05 Hz to 100Hz, while the signal-to-noise ratio in the system’s heart sound channel was 25.6 dB. Thus, the system’s accuracy was confirmed. The system test of the PCG-ECG Synchronous Auscultation System demonstrated that the P-QRS-T waveform of the ECG signal was distinctly visible, and the waveforms of the first heart sound (S1) and second heart sound (S2) of the heart sound signal were crisp and intact.

The biomedical acoustic signal plays an important role in clinical non-invasive diagnosis. In view of the deficiencies in early diagnosis of cardiovascular diseases, acoustic properties of S₁ and S₂ heart sounds are utilized. In this paper, we propose an integrated concave cilium MEMS heart sound sensor. The concave structure enlarges the area for receiving sound waves to improve the low-frequency sensitivity, and realizes the low-frequency and high-sensitivity characteristics of an MEMS heart sound sensor by adopting a reasonable acoustic package design, reducing the loss of heart sound distortion and faint heart murmurs, and improving the auscultation effect. Finally, experimental results show that the integrated concave ciliated MEMS heart sound sensor’s sensitivity reaches −180.6 dB@500 Hz, as compared with the traditional bionic ciliated MEMS heart sound sensor; the sensitivity is 8.9 dB higher. The sensor has a signal-to-noise ratio of 27.05 dB, and has good heart sound detection ability, improving the accuracy of clinical detection methods.

In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies. By rationally designing the acoustic encapsulation, the loss of heart acoustic distortion and weak cardiac murmurs is reduced. As demonstrated by experimentation, the proposed hollow MEMS structure cardiac sound sensor has a sensitivity of up to −206.9 dB at 200 Hz, showing 6.5 dB and 170 Hz increases in the sensitivity and operating bandwidth, respectively, in contrast to the planar ciliated MEMS sensor. The SNR of the sensor is 26.471 dB, showing good detectability for cardiac sounds.

Early detection of coronary artery disease (CAD) demands both high-fidelity signal acquisition and sophisticated analysis methods to capture subtle cardiac abnormalities. Traditional approaches face challenges in achieving precise synchronization and maintaining signal quality across different modalities. We address these limitations by introducing a high-sensitivity micro-electromechanical systems (MEMSs)-based synchronous auscultation system that enables unprecedented precision in simultaneous phonocardiogram–electrocardiogram (PCG-ECG). The MEMS sensors, featuring superior sensitivity, low noise floor, and excellent frequency response, capture detailed cardiac acoustic and electrical signals with minimal distortion and precise temporal alignment. Building upon this high-quality signal acquisition foundation, we propose ResNet18-Encoder-MCA (REM-Net), an innovative deep-learning framework that integrates ResNet18-based feature extraction with a mutual-cross-attention (MCA) mechanism to fully exploit the rich, synchronized cardiac information. Extensive experiments demonstrate that our MEMS-enabled approach achieves exceptional performance, with 98.81% accuracy (Acc) and 98.83% F1 Score on our collected data. The framework’s robustness is further validated on the 2016 PhysioNet/CinC Challenge dataset, achieving 99.05% Acc and 98.79% F1 Score. These results demonstrate that the combination of high-precision MEMS-based signal acquisition and advanced deep-learning analytics not only advances the state-of-the-art (SOTA) in CAD diagnosis but also provides a reliable, noninvasive, and cost-effective solution for clinical applications. Our approach represents a significant step forward in early CAD screening, where the superior signal quality enabled by MEMS technology proves crucial for accurate diagnosis.

Whispering-gallery-mode (WGM) optical microcavity acoustic sensors have attracted extensive attention owing to their high sensitivity, broad bandwidth, miniaturization and anti-electromagnetic interference capability. This review systematically surveys medium-adapted acoustic sensing and function extension applications of WGM microcavities. Starting from perturbation theory, we distinguish between optical quality factor Q₀ and mechanical quality factor Q_m, and elucidate the intrinsic sensitivity–bandwidth trade-off dominated by mechanical dynamics. We summarize advances in air-coupled and liquid-phase acoustic detection, photoacoustic imaging, and multiparameter multifunctional sensing. Practical bottlenecks including calibration deficiency, thermal drift-induced crosstalk, packaging trade-offs, long-term stability and array scalability are analyzed. Joint hardware–process–algorithm optimization strategies are discussed, providing references for advancing WGM microcavity acoustic sensors toward real-world engineering deployment.