Aortic valve opening (AO) events are crucial for detecting frequency and rhythm disorders, especially in real-world settings where seismocardiography (SCG) signals collected via consumer smartphones are subject to noise, motion artifacts, and variability caused by device heterogeneity. In this work, we present a robust deep-learning framework for SCG segmentation and rhythm analysis using accelerometer recordings obtained with consumer smartphones. We develop an enhanced U-Net v3 architecture that integrates multi-scale convolutions, residual connections, and attention gates, enabling reliable segmentation of noisy SCG signals. A dedicated post-processing pipeline converts probability masks into precise AO timestamps, whereas a novel adaptive 3D-to-1D projection method ensures robustness to arbitrary smartphone orientation. Experimental results demonstrate that the proposed method achieves consistently high accuracy and robustness across various device types and unsupervised data-collection conditions. Our approach enables practical, low-cost, and automated cardiac-rhythm monitoring using everyday mobile devices, paving the way for scalable, field-deployable cardiovascular assessment and future multimodal diagnostic systems.

翻译：主动脉瓣开放事件对于检测频率与节律障碍至关重要，尤其在现实场景中，通过消费级智能手机采集的心震图信号易受噪声、运动伪影及设备异构性引起的变异性影响。本研究提出一种基于消费级智能手机加速度计记录的鲁棒深度学习框架，用于SCG信号分割与节律分析。我们开发了增强型U-Net v3架构，该架构融合多尺度卷积、残差连接与注意力门控机制，实现对含噪SCG信号的可靠分割。专用后处理流程将概率掩码转换为精确的AO时间戳，而新颖的自适应三维至一维投影方法确保了对任意智能手机朝向的鲁棒性。实验结果表明，所提方法在不同设备类型及无监督数据采集条件下均能保持稳定的高精度与鲁棒性。该方法实现了基于日常移动设备的实用化、低成本、自动化心律监测，为可扩展的现场部署心血管评估及未来多模态诊断系统奠定了基础。