用手机加深度学习，自动检测心脏节律异常
SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions
- 改进U-Net结构，融合多尺度卷积与注意力机制
- 在多种手机和噪声条件下准确识别心跳开启时刻
- 无需校准，适合野外和普通用户使用
主动脉瓣开放（AO）事件对检测心率与节律紊乱至关重要。在真实场景中，通过消费级智能手机采集的声心动图（SCG）信号易受噪声、运动伪影及设备差异影响。本文提出一种鲁棒的深度学习框架，利用智能手机加速度计数据进行SCG分割与节律分析。开发了集成多尺度卷积、残差连接和注意力门的增强型U-Net v3架构，可有效分割噪声干扰下的SCG信号。设计专用后处理流程将概率掩码转化为精确的AO时间戳，并引入新型自适应3D到1D投影方法，确保对任意手机朝向的鲁棒性。实验表明，该方法在不同设备类型和无监督采集条件下均保持高精度与强鲁棒性。本方案实现了低成本、自动化的心脏节律监测，为可扩展的现场心血管评估及未来多模态诊断系统奠定基础。
原文摘要 · Abstract (English)
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.
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