LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics

• URL: http://arxiv.org/abs/2608.03690v1
• Date: Tue, 04 Aug 2026 13:58:10 GMT
• Title: LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics
• Abstract summary: We present WeEF (Lead-Agnostic ECG Foundation), a 7M- ECG foundation model that can capture any lead without zero-p or zero-padded modifications.<n>We train 9.2M 12-lead ECGs via masked node modelling with lead sampling, LAEF learns representations robust to lead configuration.<n>It outperforms all zero-importance datasets with a single randomly sampled lead and on 14 out of 18 with 2 leads, with an average AUROC gain of +3.2 points.
• Score: 11.969677810531152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2 leads, yet existing ECG foundation models are architecturally constrained to fixed 12-lead inputs, degrading or failing under these reduced configurations. We introduce LAEF (Lead-Agnostic ECG Foundation), a 7M-parameter ECG foundation model that can natively process any lead subset without zero-padding or architectural modification. LAEF represents ECGs as variable-size spatiotemporal graphs with physiologically motivated intra- and inter-lead connectivity, processed by a Graph Attention Network that scales naturally with active lead count.Pre-trained on 9.2M 12-lead ECGs via masked node modelling with stochastic lead sampling, LAEF learns representations robust to lead configuration. Across 18 downstream datasets, LAEF is on par with specialized 12-lead baselines over 12$\times$ larger at full lead availability. Under direct point-of-care-oriented diagnostics (1--2 leads), it outperforms all zero-padded alternatives on 17 out of 18 datasets with with a single randomly sampled lead and on 14 out of 18 with 2 leads, with an average AUROC gain of +3.2 points. Representation analysis links this advantage to architectural lead-agnosticism, and a lead-importance study across 164 cardiovascular conditions shows population-level performance is stable across single standard input leads while still recovering established clinically lead-condition associations.

Related papers

• ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning [50.82313761980655]
  ECG-LLM is an ECG-conditioned large language model trained across four cohorts 679,112 ECG studies from 186,409 patients.<n>It is trained on clinically structured question-answer pairs derived from ECG signals, clinical context, CMR, and ECHO.<n>It successfully recovers conventional ECG measurements, such as heart rate, and strongly predicts complex CMR-derived phenotypes.
  arXiv  Detail & Related papers  (2026-07-15T11:06:35Z)
• Learning Cardiac Latent Representations in Vectorcardiogram Space [54.12639543496968]
  Existing methods operate almost exclusively in the observable ECG signal space.<n>We propose learning a unified latent representation of cardiac electrical activity directly in the VCG space.<n>We introduce LVCG, the first general self-supervised representation learning framework designed to operate in this physically grounded latent space.
  arXiv  Detail & Related papers  (2026-05-29T12:48:12Z)
• Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis [45.00216485193071]
  Rare cardiac anomalies are difficult to detect from electrocardiograms due to their long-tailed distribution.<n>We developed an AI-assisted two-stage ECG framework integrating self-supervised anomaly detection and demographic-aware representation learning.<n>Our method achieves an AUROC of 94.7% for rare anomalies and reduces the common-rare performance gap by 73%.
  arXiv  Detail & Related papers  (2026-03-20T07:04:00Z)
• CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models [13.613519337591507]
  Single-lead ECG recording is integrated into both clinical-grade and consumer wearables.<n>While self-supervised pretraining of foundation models on unlabeled ECGs improves diagnostic performance, existing approaches do not incorporate domain knowledge from clinical metadata.<n>We introduce a novel contrastive learning approach that utilizes an established clinical risk score to adaptively weight negative pairs.
  arXiv  Detail & Related papers  (2025-12-01T20:21:44Z)
• An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals [0.0]
  Anomaly detection in 12-lead electrocardiograms (ECGs) is critical for identifying deviations associated with cardiovascular disease.<n>To the best of our knowledge, this study reports the first application of a VAE-BiLSTM-MHA architecture to ECG anomaly detection.
  arXiv  Detail & Related papers  (2025-10-07T13:30:02Z)
• Translation from Wearable PPG to 12-Lead ECG [35.793551592077016]
  The 12-lead electrocardiogram (ECG) is the gold standard for cardiovascular monitoring.<n>Existing 12-lead ECG systems rely on cumbersome multi-electrode setups, limiting sustained monitoring in ambulatory settings.<n>We introduce P2Es, an innovative demographic-aware framework designed to generate clinically valid 12-lead ECG from PPG signals.
  arXiv  Detail & Related papers  (2025-09-29T20:36:24Z)
• GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images [44.50428701650495]
  We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation.<n> GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations.<n>We propose the Grounded ECG task, a clinically motivated benchmark designed to assess the MLLM's capability in grounded ECG understanding.
  arXiv  Detail & Related papers  (2025-03-08T05:48:53Z)
• Multimodal Latent Fusion of ECG Leads for Early Assessment of Pulmonary Hypertension [30.124231086488976]
  We propose a lead-specific electrocardiogram multimodal variational autoencoder (textscLS-EMVAE)<n>textscLS-EMVAE incorporates a hierarchical modality expert (HiME) fusion mechanism and a latent representation alignment loss.<n>We validate textscLS-EMVAE across two retrospective cohorts in a 6L-ECG setting.
  arXiv  Detail & Related papers  (2025-03-03T16:16:38Z)
• Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture [0.0]
  We introduce ECG-JEPA, a self-supervised learning model for 12-lead ECG analysis.<n>It learns semantic representations of ECG data by predicting in the hidden latent space.<n> ECG-JEPA achieves state-of-the-art performance in various downstream tasks including ECG classification and feature prediction.
  arXiv  Detail & Related papers  (2024-10-11T06:30:48Z)
• Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG [19.74009541199362]
  This study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG. 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.
  arXiv  Detail & Related papers  (2024-07-16T08:17:45Z)
• ECGNet: A generative adversarial network (GAN) approach to the synthesis of 12-lead ECG signals from single lead inputs [0.0]
  ECGNet is a procedure that generates a complete set of 12-lead ECG signals from any single lead input. To the best of our knowledge, ECGNet is the first to predict all of the remaining eleven leads from the input of any single lead.
  arXiv  Detail & Related papers  (2023-09-23T16:43:31Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble [64.29529357862955]
  We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
  arXiv  Detail & Related papers  (2020-10-21T18:11:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.