TELL ME: Tackle Electrocardiogram with Large Language Model Effectively

Tianyi Shi, Siyang Zheng, Zhu Meng, Zhe Cui, Jin Huang, Changrui Ren, Bo Zhang, Zhicheng Zhao

Abstract: Electrocardiogram (ECG) signals are crucial indicators of various human physiological states. A thorough analysis of these signals is indispensable for applications such as disease prediction, mental stress assessment, and other medical diagnostics. Despite the rapid progress in large language models (LLM) and their demonstrated prowess in natural language understanding, their application in ECG signal analysis remains underexplored. This paper introduces Tackle Electrocardiogram with Large Language Model Effectively (TELL ME), a novel approach that effectively transfers the robust comprehension capabilities of LLM to ECG signal processing. The method employs a front alignment strategy to align ECG modality with text modality via cross-attention mechanism and incorporates critical manual features into prompts to enhance the performances in specific tasks. The proposed solution has been validated across three downstream tasks, namely, quality assessment, ventricular premature beats detection, and denoising reconstruction, consistently achieving state-of-the-art (SOTA) results.

External IDs:dblp:conf/icassp/ShiZMCHRZZ25