ECG-aBcDe: Overcoming Model Dependence, Encoding ECG into a Universal Language for Any LLM

• URL: http://arxiv.org/abs/2509.12625v1
• Date: Tue, 16 Sep 2025 03:41:02 GMT
• Title: ECG-aBcDe: Overcoming Model Dependence, Encoding ECG into a Universal Language for Any LLM
• Authors: Yong Xia, Jingxuan Li, YeTeng Sun, Jiarui Bu,
• Abstract summary: Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis.<n>Current methods suffer from model-specific ECG encoders, hindering transfer across LLMs.<n>We introduce ECG-aBcDe, a novel encoding method that transforms ECG signals into a universal ECG language readily interpretable by any LLM.
• Score: 7.632459372363093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis, yet challenges remain regarding transferability, time-scale information learning, and interpretability. Current methods suffer from model-specific ECG encoders, hindering transfer across LLMs. Furthermore, LLMs struggle to capture crucial time-scale information inherent in ECGs due to Transformer limitations. And their black-box nature limits clinical adoption. To address these limitations, we introduce ECG-aBcDe, a novel ECG encoding method that transforms ECG signals into a universal ECG language readily interpretable by any LLM. By constructing a hybrid dataset of ECG language and natural language, ECG-aBcDe enables direct fine-tuning of pre-trained LLMs without architectural modifications, achieving "construct once, use anywhere" capability. Moreover, the bidirectional convertibility between ECG and ECG language of ECG-aBcDe allows for extracting attention heatmaps from ECG signals, significantly enhancing interpretability. Finally, ECG-aBcDe explicitly represents time-scale information, mitigating Transformer limitations. This work presents a new paradigm for integrating ECG analysis with LLMs. Compared with existing methods, our method achieves competitive performance on ROUGE-L and METEOR. Notably, it delivers significant improvements in the BLEU-4, with improvements of 2.8 times and 3.9 times in in-dataset and cross-dataset evaluations, respectively, reaching scores of 42.58 and 30.76. These results provide strong evidence for the feasibility of the new paradigm.

Related papers

• ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation [36.244601234085856]
  Existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation.<n>ECG-R1 is the first reasoning MLLM designed for reliable ECG interpretation.<n>Code and data are publicly available at hrefhttp://ai.heartvoice.com.cn/ECG-R1here.
  arXiv  Detail & Related papers  (2026-02-04T07:17:55Z)
• EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model [46.84040404474695]
  EnECG is an ensemble-based framework that integrates multiple specialized foundation models, each excelling in different aspects of ECG interpretation.<n>We show that EnECG can help reduce computational and memory costs while maintaining the strong representational power of foundation models.<n>This framework not only enhances feature extraction and predictive performance but also ensures practical efficiency for real-world clinical applications.
  arXiv  Detail & Related papers  (2025-11-28T07:22:33Z)
• Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation [52.19347532840774]
  We propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for ECG generation.<n> SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder.<n>Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment.
  arXiv  Detail & Related papers  (2025-11-13T02:57:10Z)
• UniECG: Understanding and Generating ECG in One Unified Model [26.641666246045133]
  We propose UniECG, the first unified model for ECG capable of concurrently performing evidence-based ECG interpretation and text-conditioned ECG generation tasks.<n>UniECG can autonomously choose to interpret or generate an ECG based on user input, significantly extending the capability boundaries of current ECG models.
  arXiv  Detail & Related papers  (2025-09-23T03:15:53Z)
• 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)
• 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)
• Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling [50.58126509704037]
  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.
  arXiv  Detail & Related papers  (2025-06-06T07:56:41Z)
• 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)
• ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling [20.484166589932702]
  Large Language Models (LLMs) have demonstrated exceptional versatility across domains, including applications to electrocardiograms (ECGs)<n>We propose ECG-Byte, an adapted byte pair encoding (BPE) tokenizer pipeline for autoregressive language modeling of ECGs.<n>We achieve competitive NLG performance while training 3 times faster and using just 48% of the data required by traditional two-stage methods.
  arXiv  Detail & Related papers  (2024-12-18T22:13:21Z)
• AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings [34.078819572852446]
  Electrocardiogram (ECG) is highly sensitive in detecting acute heart attacks.<n>This paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data.
  arXiv  Detail & Related papers  (2024-11-17T17:32:58Z)
• Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning [19.513904491604794]
  Electrocardiogram (ECG) interpretation requires specialized expertise.<n>This work introduces a novel multimodal meta-learning method for few-shot ECG question answering.
  arXiv  Detail & Related papers  (2024-10-18T13:48:01Z)
• MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [28.35107188450758]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions.<n>Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation.<n>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)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

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.