Vib2ECG：一种配对的胸导体SCG-ECG数据集和用于ECG重建的基准
Vib2ECG: A Paired Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction
AI总结：
本文提出Vib2ECG数据集，用于从低成本振动信号重建ECG，展示了轻量U-Net在多导联ECG重建中的可行性，并分析了模型生成虚假ECG波形的问题。
摘要：
十二导联心电图（ECG）对心血管诊断至关重要，但日常生活中长期获取受限于复杂昂贵的硬件。最近的研究尝试从低成本心振动信号（如心音图SCG）重建ECG，但缺乏数据集限制了方法仅适用于肢体导联，而临床诊断需要多导联ECG，包括胸导联。本文提出Vib2ECG，首个配对多通道电机械心脏信号数据集，包含17名受试者在六个胸导位通过惯性测量单元（IMUs）获取的完整十二导联ECG和振动信号。基于此数据集，我们还提供了一个基准。实验结果表明，使用轻量364K参数U-Net可以从振动信号中重建不同位置的电信号，但模型会产生虚假ECG波形，我们在无对应电活动区域生成波形。我们分析了该现象的原因并提出缓解方向。本研究展示了通过低成本IMU传感器获取的胸导ECG预测实现移动设备友好的ECG监测的可行性。它扩展了心振动信号的应用，并提供了关于心脏电和机械活动空间关系的新见解。
英文摘要：
Twelve-lead electrocardiography (ECG) is essential for cardiovascular diagnosis, but its long-term acquisition in daily life is constrained by complex and costly hardware. Recent efforts have explored reconstructing ECG from low-cost cardiac vibrational signals such as seismocardiography (SCG), however, due to the lack of a dataset, current methods are limited to limb leads, while clinical diagnosis requires multi-lead ECG, including chest leads. In this work, we propose Vib2ECG, the first paired, multi-channel electro-mechanical cardiac signal dataset, which includes complete twelve-lead ECGs and vibrational signals acquired by inertial measurement units (IMUs) at six chest-lead positions from 17 subjects. Based on this dataset, we also provide a benchmark. Experimental results demonstrate the feasibility of reconstructing electrical cardiac signals at variable locations from vibrational signals using a lightweight 364 K-parameter U-Net. Furthermore, we observe a hallucination phenomenon in the model, where ECG waveforms are generated in regions where no corresponding electrical activity is present. We analyze the causes of this phenomenon and propose potential directions for mitigation. This study demonstrates the feasibility of mobile-device-friendly ECG monitoring through chest-lead ECG prediction from low-cost vibrational signals acquired using IMU sensors. It expands the application of cardiac vibrational signals and provides new insights into the spatial relationship between cardiac electrical and mechanical activities with spatial location variation.
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