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), with prior studies primarily focusing on limb leads, while clinical diagnosis requires multi-lead ECG, including chest leads. In this work, we propose Vib2ECG, a dataset of synchronized, co-located ECG and chest-wall vibration recordings, which includes complete twelve-lead ECGs and vibrational signals acquired by inertial measurement units (IMUs) at the six standard precordial electrode sites (V1-V6) 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 364K-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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