# Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework

arXiv: https://arxiv.org/abs/2505.18847 · HTML: https://mlsanity.com/paper/2505.18847/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Time Series as Images: Vision Transformer for Irregularly Sampled Time Series](https://mlsanity.com/paper/2303.12799/) (2023)
- [Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement](https://mlsanity.com/paper/2403.06659/) (2024)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram](https://mlsanity.com/paper/2203.06889/) (2022)
- [ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis](https://mlsanity.com/paper/2408.08849/) (2024)
- [Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model](https://mlsanity.com/paper/2502.10707/) (2025)
- [Teach Multimodal LLMs to Comprehend Electrocardiographic Images](https://mlsanity.com/paper/2410.19008/) (2024)
- [Harnessing Vision Models for Time Series Analysis: A Survey](https://mlsanity.com/paper/2502.08869/) (2025)
- [Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?](https://mlsanity.com/paper/2301.09017/) (2023)
- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series](https://mlsanity.com/paper/2003.12469/) (2020)
- [ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling](https://mlsanity.com/paper/2412.14373/) (2024)
- [Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling](https://mlsanity.com/paper/2409.08788/) (2024)
- [Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning](https://mlsanity.com/paper/2410.14464/) (2024)
- [HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes](https://mlsanity.com/paper/2212.14040/) (2022)
- [Interpretation of Intracardiac Electrograms Through Textual Representations](https://mlsanity.com/paper/2402.01115/) (2024)

## Built on by (most cited first, 3 of 3)

- [Retrieval-Augmented Generation for Electrocardiogram-Language Models](https://mlsanity.com/paper/2510.00261/) (2025)
- [ELF: A Family of Encoder-Free ECG-Language Models](https://mlsanity.com/paper/2601.18798/) (2026)
- [ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution](https://mlsanity.com/paper/2610.17718/) (2026)

## Abstract (arXiv)

Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-Language Models (ELMs). Conditioned on an ECG and a textual query, an ELM autoregressively generates a free-form textual response. Unlike traditional classification-based systems, ELMs emulate expert cardiac electrophysiologists by issuing diagnoses, analyzing waveform morphology, identifying contributing factors, and proposing patient-specific action plans. To realize this potential, researchers are curating instruction-tuning datasets that pair ECGs with textual dialogues and are training ELMs on these resources. Yet before scaling ELMs further, there is a fundamental question yet to be explored: What is the most effective ECG input representation? In recent works, three candidate representations have emerged-raw time-series signals, rendered images, and discretized symbolic sequences. We present the first comprehensive benchmark of these modalities across 6 public datasets and 5 evaluation metrics. We find symbolic representations achieve the greatest number of statistically significant wins over both signal and image inputs. We further ablate the LLM backbone, ECG duration, and token budget, and we evaluate robustness to signal perturbations. We hope that our findings offer clear guidance for selecting input representations when developing the next generation of ELMs.