REVIEW 12 cited by
An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/PKUDigitalHealth/ECGFounder
Forward citations
Cited by 12 Pith papers
-
Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning
xMAE pretrains biosignal representations via masked cross-modal reconstruction of temporally ordered signals like ECG and PPG, outperforming baselines on 15 of 19 downstream tasks including cardiovascular prediction a...
-
Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection
For Brugada syndrome detection, ECG foundation-model pre-training mainly stabilizes optimization rather than encoding transferable clinical knowledge, and fails to improve zero-shot cross-site generalization.
-
Learning Cardiac Latent Representations in Vectorcardiogram Space
LVCG is the first self-supervised framework for learning view-invariant latent VCG representations that claims to outperform ECG-space baselines with better robustness and generalization in domain shift settings.
-
VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
VitalAgent adds longitudinal memory and tool-augmented reasoning to an agent for reactive QA and proactive monitoring on ECG/PPG streams, reporting >25% gains over baselines on a new 1,862-pair + 90-hour benchmark.
-
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.
-
How Do Electrocardiogram Models Scale?
Empirical scaling study of ECG models finds SSL scales robustly while ResNets show 1.3-2.5x better parameter efficiency and SSL up to 16x better data efficiency than supervised baselines on out-of-distribution tasks.
-
EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning
EduGage releases a multimodal sensor dataset and models for estimating learner engagement in self-guided video learning, reporting MAE of 0.81 and outperforming baselines with 16 participants.
-
Towards Real-Time ECG and EMG Modeling on $\mu$NPUs
PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
-
Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI
DeepHHF, trained on day-long single-lead Holter ECGs from 40,174 patients, predicted incident heart failure within five years with AUROC 0.80 and external AUROC 0.81.
-
LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
LSTrans delivers competitive multi-label ECG AUC and Fβ=2 scores on three clinical benchmarks while cutting peak GPU memory and training iteration time via an interleaved 1D backbone, dual-rank LoRA, and homogeneous/h...
-
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
A parameter-efficient plug-in framework adds structurally compatible long-sequence processing and semantically informed temporal modeling to extend pretrained 10-second ECG foundation models to longer variable-length inputs.
-
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
Discussion (0). Sign in to comment.