Vib2ECG: A Paired Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction

2026-03-16

AI Summary

This study addresses the challenge of reconstructing clinically relevant multi-lead electrocardiograms (ECGs) from low-cost vibrational signals, such as seismocardiography (SCG), which has been hindered by the scarcity of synchronized ECG–vibration data. To overcome this limitation, the authors present Vib2ECG, the first simultaneously acquired multimodal dataset comprising 12-lead ECG and inertial measurement unit (IMU)-based vibration signals from six precordial lead positions across 17 subjects, along with a benchmark for ECG reconstruction. Using a lightweight U-Net model with only 364K parameters, they demonstrate, for the first time, the feasibility of reconstructing precordial-lead ECGs directly from IMU signals, supporting mobile-friendly, long-term cardiac monitoring. The work also identifies and analyzes a “hallucination” phenomenon—where the model generates plausible ECG waveforms in electrically silent regions—offering new insights into cardio-electromechanical coupling mechanisms.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• ECG reconstruction
• seismocardiography
• chest-lead ECG
• cardiac vibrational signals
• twelve-lead ECG

Innovation

• ECG reconstruction
• seismocardiography (SCG)
• inertial measurement unit (IMU)
• multi-lead ECG
• cardiac mechanical-electrical coupling