Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

• URL: http://arxiv.org/abs/2506.05831v2
• Date: Mon, 09 Jun 2025 08:22:41 GMT
• Title: Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling
• Authors: Yihan Xie, Sijing Li, Tianwei Lin, Zhuonan Wang, Chenglin Yang, Yu Zhong, Wenqiao Zhang, Haoyuan Li, Hao Jiang, Fengda Zhang, Qishan Chen, Jun Xiao, Yueting Zhuang, Beng Chin Ooi,
• Abstract summary: Heartcare Suite is a framework for fine-grained electrocardiogram (ECG) understanding.<n>Heartcare-220K is a high-quality, structured, and comprehensive multimodal ECG dataset.<n>Heartcare-Bench is a benchmark to guide the optimization of Medical Multimodal Large Language Models (Med-MLLMs) in ECG scenarios.
• Score: 50.58126509704037
• License: http://creativecommons.org/licenses/by/4.0/
• 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 .

Related papers

• MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations [12.00096975933262]
  Electrocardiogram (ECG) plays a foundational role in modern cardiovascular care, enabling non-invasive diagnosis of arrhythmias, myocardial ischemia, and conduction disorders.<n>Most existing ECG datasets provide only single-modality data or, at most, dual modalities, making it difficult to build models that can understand and integrate diverse ECG information in real-world settings.<n>We introduce MEETI, the first large-scale ECG dataset that synchronizes raw waveform data, high-resolution plotted images, and detailed textual interpretations generated by large language models.
  arXiv  Detail & Related papers  (2025-07-21T05:32:44Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals [36.08910150609342]
  We present a cardiac sensing foundation model (CSFM) that learns unified representations from vast, heterogeneous health records.<n>Our model is pretrained on an innovative multi-modal integration of data from multiple large-scale datasets.<n> CSFM consistently outperforms traditional one-modal-one-task approaches.
  arXiv  Detail & Related papers  (2025-06-23T20:58:12Z)
• From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining [22.214252217020174]
  We introduce MELP, a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs.<n>We evaluate MELP on three public ECG datasets across multiple tasks, including zero-shot ECG classification, linear probing, and transfer learning.
  arXiv  Detail & Related papers  (2025-06-11T07:22:17Z)
• CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture [0.40964539027092906]
  We present CardioPatternFormer, a Transformer-based model for interpretable ECG classification.<n>It employs a sophisticated attention mechanism to precisely identify and classify diverse cardiac patterns.<n>It excels at discerning subtle anomalies and distinguishing multiple co-occurring conditions.
  arXiv  Detail & Related papers  (2025-05-26T19:36:58Z)
• GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images [43.65650710265957]
  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)
• An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains [17.809094003643523]
  ECG Foundation Model (ECGFounder) trained on over 10 million ECGs with 150 label categories from Harvard-Emory ECG Database.<n>ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses.<n>When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis.
  arXiv  Detail & Related papers  (2024-10-05T12:12:02Z)
• Sequence-aware Pre-training for Echocardiography Probe Movement Guidance [71.79421124144145]
  We introduce a novel probe movement guidance algorithm that has the potential to be applied in guiding robotic systems or novices with probe pose adjustment for high-quality standard plane image acquisition.<n>Our approach learns personalized three-dimensional cardiac structural features by predicting the masked-out image features and probe movement actions in a scanning sequence.
  arXiv  Detail & Related papers  (2024-08-27T12:55:54Z)
• ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text [14.06147507373525]
  This study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI)
  arXiv  Detail & Related papers  (2024-05-26T06:45:39Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)

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.