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

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.

Figures (7)

• Figure 1: Conceptual overview of SCG-to-ECG reconstruction for mobile and wearable cardiac monitoring. While SCG offers a cost-effective sensing modality, its limited interpretability motivates the reconstruction of ECG signals. Existing approaches are constrained by the lack of paired chest-lead SCG-ECG datasets. This work addresses these gaps by providing a paired chest-lead dataset based on twelve-lead system and establishing a SCG-to-ECG reconstruction benchmark.
• Figure 2: The overview of the model and data collection approach. Left: In the hardware setup, an IMU is attached to an electrode buckle to form an electromechanical sensing pair that records colocated ECG and vibrational signals at six positions (V1–V6). All pairs are controlled by a single FPGA with a shared global clock, enabling timestamp-based interpolation for cross-modal alignment. Middle: The acquired vibration signals are decomposed into low-frequency signals (SCG, 2–20 Hz) and high-frequency phonocardiogram-like components (PCGL, $>$20 Hz), which are normalized separately and then together used as model inputs. Right: The baseline U-Net model takes SCG and PCGL as inputs to reconstruct the corresponding ECG signals, supervised by the aligned ground-truth ECG through a reconstruction loss.
• Figure 3: SCG (0-20 Hz) contains over 96% of the total energy of the original signal. If the PCG-like (PCGL) component (above 20 Hz) is not separately extracted and normalized, it will be invisible within the original waveform, and the information carried in the PCGL band will be ignored.
• Figure 4: A typical comparative example with different inputs. (a) Reconstruction with only SCG as input. The outputs are less precise in amplitude and timing but exhibit fewer hallucinations, i.e., generated ECG waveforms that do not actually exist. (b) Reconstruction with SCG and PCGL as inputs. The outputs are precise and exhibit fewer hallucinations. (c) Reconstruction with only PCGL as input. The outputs are more precise but exhibit more hallucinations.
• Figure 5: (a) Hallucination percentage with different inputs. “PCGL” and “SCG” denote models using a single normalized PCGL or SCG channel as input, respectively. “Raw” indicates the use of a single channel of the original acceleration signal without decomposition. “Both” represents a two-channel input consisting of normalized PCGL and SCG signals. (b) L1 distance with different inputs. (c) Hallucination percentage with different time interval between training and testing data. (d) L1 distance with different time interval between training and testing data.