Model Card for MELP
MELP (Multi-scale ECG-Language Pretraining) is a novel model presented in the paper "From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining". It aims to overcome limitations in traditional ECG analysis by leveraging hierarchical supervision from ECG-text pairs to align ECG signals with textual reports.
Model Details
Model Description
Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks.
MELP introduces a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs. MELP first pretrains a cardiology-specific language model to enhance its understanding of clinical text. It then applies three levels of cross-modal supervision—at the token, beat, and rhythm levels—to align ECG signals with textual reports, capturing structured information across different time scales. Experimental results demonstrate that MELP outperforms existing SSL methods, underscoring its effectiveness and adaptability across diverse clinical applications.
- Developed by: The authors of the paper.
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: Multimodal ECG-Language Pretraining Model
- Language(s) (NLP): English (clinical text)
- License: Apache-2.0
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: The code is available as mentioned in the paper's abstract. Please refer to the paper for the exact URL.
- Paper [optional]: From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
- Demo [optional]: [More Information Needed]
Uses
Direct Use
MELP can be directly used for self-supervised learning of robust ECG representations. These representations can be efficiently transferred to various downstream tasks, such as zero-shot ECG classification, linear probing, and other transfer learning applications on ECG data.
Downstream Use [optional]
The model can be fine-tuned for diverse clinical applications, including but not limited to tasks that require aligning ECG signals with textual reports, thereby assisting in cardiac health monitoring and heart disease diagnosis.
Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
1from transformers import AutoConfig, AutoModel
2
3# This is a placeholder for the actual model ID on the Hugging Face Hub.
4# Replace "your_model_id" with the correct model identifier.
5model_id = "your_model_id" # e.g., "org/melp-model"
6
7# Load configuration
8config = AutoConfig.from_pretrained(model_id)
9
10# Load model
11model = AutoModel.from_pretrained(model_id, config=config)
12
13# For detailed usage instructions and examples, please refer to the paper's
14# official code repository mentioned in the abstract.
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
1@article{zhou2025token,
2 title={From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining},
3 author={Zhou, Zijian and Liu, Shikun and Han, Xiao and Liu, Haozhe and Ng, Kam Woh and Xie, Tian and Cong, Yuren and Li, Hang and Xu, Mengmeng and P{\'e}rez-R{\'u}a, Juan-Manuel and Patel, Aditya and Xiang, Tao and Shi, Miaojing and He, Sen},
4 journal={arXiv preprint arXiv:2506.21803},
5 year={2025}
6}
APA:
Zhou, Z., Liu, S., Han, X., Liu, H., Ng, K. W., Xie, T., Cong, Y., Li, H., Xu, M., Pérez-Rúa, J.-M., Patel, A., Xiang, T., Shi, M., & He, S. (2025). From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining. arXiv preprint arXiv:2506.21803.
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