Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework (2505.18847v1)

Abstract: Recent advances have increasingly applied LLMs to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-LLMs (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.

Signal, Image, or Symbolic: Exploring Input Representations for ELMs

The paper titled "Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-LLMs Through a Unified Framework" explores the efficacy of different input modalities for Electrocardiogram-LLMs (ELMs). With the rising interest in employing LLMs to innovate medical diagnostics, particularly in the interpretation of electrocardiograms (ECGs), the paper provides a systematic comparison of three distinct input representations—raw signals, graphical images, and symbolic data—within multimodal learning paradigms.

Key Findings

1. Representation Efficacy: Through a comprehensive benchmark across six datasets and various performance metrics, the symbolic representation (ECG-Byte) demonstrated superior performance across all evaluation criteria. This representation, which transforms ECG signals into tokenized data through quantization and encoding, significantly outperformed both the raw signal and image modalities in generative tasks.
2. Statistical Analysis: The work conducted a rigorous statistical analysis, identifying significant differences in the performance of ECG representations. The symbolic approach using ECG-Byte achieved the highest number of statistically significant results compared to traditional signal and image methods.
3. Ablation Studies: The research included extensive ablation studies that assessed the robustness and scalability of each representation under various conditions. It highlighted that although symbolic representations yielded the best performance, its superiority persisted with extended ECG lengths and was notably resilient to signal perturbations.
4. Implications on Architectures: The findings suggest that when developing ELMs, symbolic inputs offer optimal utilization of model capacities in autoregressive text generation settings. The ability to directly leverage token-based representations without requiring an intermediate encoder simplifies the model pipeline and optimizes computational resources.
5. Training Paradigms: The paper explored multiple training methodologies, including conventional 2-stage training for model-specific encoders and end-to-end finetuning of LLMs. It underscored that the end-to-end symbolic approach was most effective for achieving high generative accuracy within the constraints of modern LLM architectures.

Theoretical and Practical Implications

Theoretically, the paper reinforces the viability of symbolic representation in multimodal machine learning, where integration across heterogeneous data types poses significant challenges. By aligning ECG signals with textual embeddings, this method could pave the way for more nuanced diagnostics and real-time patient feedback systems.

Practically, employing symbolic representation can streamline the deployment of ELMs in clinical settings, minimizing dependence on heavy computational resources. This is particularly beneficial for enhancing accessibility to expert-level diagnostics in low-resource environments, addressing the clinical workload exacerbated by a global shortage of skilled electrophysiologists.

Future Directions

The paper suggests several promising avenues for future research. These include refining symbolic representation techniques to compress broader datasets without loss of critical patient information, exploring hybrid models that integrate symbolic with auxiliary modalities for comprehensive diagnostics, and scaling these models to tackle the full spectrum of cardiac conditions robustly.

Overall, by presenting a compelling case for symbolic representation in ECG analysis, this work lays a foundational framework for the next generation of ELMs, fostering innovation in AI-driven healthcare diagnostics.