ArticleNPJ cardiovascular health2024
Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG.
Article in NPJ cardiovascular health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diagnostic Feature Reconstruction for Enhanced Single-Lead ECG Classification.Sensors (Basel, Switzerland) · 2026Article
- Associations of psychosocial factors with cardiovascular health in aging: insights from the Inlife-Aging Project.GeroScience · 2026Article
- Reconstructing 12-lead ECG from 3-lead ECG using variational autoencoder to improve cardiac disease detection of wearable ECG devices.PLOS digital health · 2026Article
- CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.NPJ digital medicine · 2026Article
- Contactless 12-lead electrocardiogram via deep computational radar.npj biomedical innovations · 2026Article
- Deep Learning-Based Multi-Lead ECG Reconstruction from Lead I with Metadata Integration and Uncertainty Estimation.Sensors (Basel, Switzerland) · 2025Article
- MS-LTCAF: A Multi-Scale Lead-Temporal Co-Attention Framework for ECG Arrhythmia Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease.World journal of cardiology · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
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 0.0175 and 0.0654, Pearson correlation coefficients of 0.7772 and 0.7287. 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.
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Registered trials
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