Phonocardiogram (PCG) has increasingly been applied to out-of-hospital monitoring and home health management. Among these, Bone-Conduction PCG (BCPCG) has emerged as a promising solution for long-term wearable cardiac sound monitoring due to its superior noise resistance and privacy protection. However, its acquisition is susceptible to variations in textile thickness and contact pressure, exacerbating instability in critical event detection. To systematically evaluate these influencing mechanisms, this study quantifies the impact of varying textile thickness (0-5.92 mm) and pressure (0-15 N) combinations on BCPCG. First, an equivalent mass-damping-spring model was employed to assess the transmission dynamics qualitatively. Subsequently, rigorous experiments were designed to collect signals and compare key features, including S1/S2 localization accuracy, pseudo signal-to-noise ratio (PSNR), and spectral centroid (SC). The results demonstrate that BCPCG signals remain relatively stable within the 0-2 N range, exhibit slight degradation between 3-5 N, and experience a marked decline under high pressure (10-15 N), where the PSNR drops by nearly 50% and S1 localization accuracy decreases to 70.27%. This may stem from tissue tremors and amplified high-frequency noise under high pressure. Meanwhile, textile thickness at low pressures primarily affects high-frequency components without significantly impacting localization accuracy. Finally, a classification model based on Top-11 features identified contact pressure intervals (0-2 N, 3-5 N, 10-15 N), and the macro-averaged AUC reached 0.985. This study validates the feasibility of pressure-inverse inference using BCPCG features, providing theoretical and practical foundations for real-world applications.
Li et al. (Thu,) studied this question.