Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling
Journal:
arXiv
Published Date:
Jun 6, 2025
Abstract
We present Heartcare Suite, a multimodal comprehensive framework for
finegrained electrocardiogram (ECG) understanding. It comprises three key
components: (i) Heartcare-220K, a high-quality, structured, and comprehensive
multimodal ECG dataset covering essential tasks such as disease diagnosis,
waveform morphology analysis, and rhythm interpretation. (ii) Heartcare-Bench,
a systematic and multi-dimensional benchmark designed to evaluate diagnostic
intelligence and guide the optimization of Medical Multimodal Large Language
Models (Med-MLLMs) in ECG scenarios. and (iii) HeartcareGPT with a tailored
tokenizer Bidirectional ECG Abstract Tokenization (Beat), which compresses raw
multi-lead signals into semantically rich discrete tokens via duallevel vector
quantization and query-guided bidirectional diffusion mechanism. Built upon
Heartcare-220K, HeartcareGPT achieves strong generalization and SoTA
performance across multiple clinically meaningful tasks. Extensive experiments
demonstrate that Heartcare Suite is highly effective in advancing ECGspecific
multimodal understanding and evaluation. Our project is available at
https://github.com/DCDmllm/Heartcare-Suite .
