anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding
This work addresses the need for more versatile ECG analysis tools in clinical and home environments, though it is incremental as it builds on existing MLLM frameworks.
The paper tackles the limitation of existing ECG-focused multimodal large language models (MLLMs) that are restricted to single 12-lead, short-duration inputs and report generation tasks, by developing anyECG-chat, a model that supports flexible ECG inputs and multi-task understanding, including report generation, abnormal waveform localization, and open-ended question answering, with evaluations showing capability in various practical scenarios.
The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often limited to single 12-lead, short-duration (10s) ECG inputs, thereby underutilizing the potential of MLLMs. To this end, we aim to develop a MLLM for ECG analysis that supports a broader range of tasks and more flexible ECG inputs. However, existing ECG-QA datasets are often monotonous. To address this gap, we first constructed the anyECG dataset, which encompasses a wide variety of tasks, including report generation, abnormal waveform localization, and open-ended question answering. In addition to standard hospital ECGs, we introduced long-duration reduced-lead ECGs for home environments and multiple ECG comparison scenarios commonly encountered in clinical practice. Furthermore, we propose the anyECG-chat model, which supports dynamic-length ECG inputs and multiple ECG inputs. We trained the model using a three-stage curriculum training recipe with the anyECG dataset. A comprehensive evaluation was conducted, demonstrating that anyECG-chat is capable of supporting various practical application scenarios, including not only common report generation tasks but also abnormal waveform localization for long-duration reduced-lead ECGs in home environments and comprehensive comparative analysis of multiple ECGs. Our code and data are available at: https://github.com/CuCl-2/anyECG-chat.