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

TL;DR

This paper introduces Vib2ECG, a novel multi-channel ECG dataset paired with SCG signals, and demonstrates the feasibility of reconstructing twelve-lead ECGs from vibrational signals using a lightweight neural network, advancing mobile cardiac monitoring.

Contribution

The creation of the first paired multi-channel ECG and SCG dataset, and a benchmark demonstrating ECG reconstruction from vibrational signals with a lightweight model.

Findings

Feasibility of reconstructing twelve-lead ECGs from vibrational signals.

Identification of a hallucination phenomenon in ECG generation.

Insights into the spatial relationship between electrical and mechanical cardiac activities.

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.