# Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG

arXiv: https://arxiv.org/abs/2407.11481 · HTML: https://mlsanity.com/paper/2407.11481/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision](https://mlsanity.com/paper/2105.06293/) (2021)

## Built on by (most cited first, 9 of 9)

- [Self-Alignment Learning to Improve Myocardial Infarction Detection from Single-Lead ECG](https://mlsanity.com/paper/2509.19397/) (2025)
- [Knowledge Knows, Verbalization Tells: Disentangling Latent Directions for Mathematical Solvability in LLMs](https://mlsanity.com/paper/2607.05013/) (2026)
- [Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography](https://mlsanity.com/paper/2509.08116/) (2025)
- [NEF-NET+: Adapting Electrocardio panorama in the wild](https://mlsanity.com/paper/2511.02880/) (2025)
- [Reconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices](https://mlsanity.com/paper/2510.11442/) (2025)
- [Approaching Low-Cost Cardiac Intelligence with Semi-Supervised Knowledge Distillation](https://mlsanity.com/paper/2511.02851/) (2025)
- [Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction](https://mlsanity.com/paper/2603.17248/) (2026)
- [Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation](https://mlsanity.com/paper/2606.23707/) (2026)
- [Evaluator-Dependent Patient-Adaptive ECG Lead-Channel Allocation](https://mlsanity.com/paper/2608.26827/) (2026)

## Abstract (arXiv)

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.