Imperial College London (Liu et al.)
Multimodal model that learns a shared representation space for ECG signals and their clinical text reports, pretrained on paired MIMIC-IV-ECG recordings and reports. Supports zero-shot ECG classification via text prompts, tested across six public benchmark datasets including PTB-XL and CPSC2018 without any downstream training data. Developed at Imperial College London and published at ICML 2024.
Architecture
Hybrid
ViT-tiny ECG signal encoder + Med-CPT text encoder, CLIP-style contrastive multimodal pretraining, with Clinical Knowledge Enhanced Prompt Engineering (CKEPE) for zero-shot inference
Framework
PyTorch
Added to catalog
2026-07-10
MIT
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
800,035 12-lead ECG-report pairs from 161,352 subjects at Beth Israel Deaconess Medical Center.
Zero-shot classification (avg. across 6 datasets)