ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

2026-08-06

AI Summary

This work proposes ECG-LENS, an end-to-end framework for multi-lead electrocardiogram (ECG) report generation aimed at alleviating clinician workload and improving diagnostic efficiency. The approach integrates a lead-aware encoder with global dependency modeling, augmented by a clinical terminology–enhanced textual prompting mechanism and an ECG-specific report preprocessing strategy to enable diagnosis-aware, context-guided text generation. Additionally, the authors introduce F1-ECGBERT, a BERT-based evaluation metric tailored to ECG report assessment. Evaluated on the PTB-XL and MIMIC-IV-ECG datasets, the model substantially outperforms existing methods, achieving relative improvements of 4.0% in METEOR, 6.3% in ROUGE-L, and 11.5% in F1-ECGBERT.

Abstract

Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging. Automating ECG report generation could reduce clinicians' interpretive workload, improve diagnostic efficiency, and expand access to cardiac assessment in underserved communities. Unlike image-based report-generation tasks, ECG interpretation requires the analysis of subtle temporal morphologies, followed by coherent diagnostic reasoning expressed in dense clinical terminology. Existing systems predominantly focus on classification, while current report-generation methods often produce outputs that remain inadequate for practical clinical use. To address these challenges, we propose ECG-LENS, an end-to-end ECG report-generation framework that jointly integrates multi-lead signal modeling, diagnosis-aware representations, and clinically grounded text generation. ECG-LENS combines lead-wise encoders that preserve localized waveform morphology with a global encoder that captures inter-lead dependencies. To guide report generation, we fuse signal representations with clinically enriched textual prompts that condition a GPT-2 decoder. We further introduce an ECG-specific report-preprocessing strategy that helps the model focus on clinically meaningful findings. Finally, because lexical metrics may under- or overestimate report quality, we propose F1-ECGBERT, a BERT-based, ECG-specific metric that measures agreement between diagnostic labels extracted from generated and reference reports. In-domain experiments on PTB-XL and cross-domain evaluation on MIMIC-IV-ECG show that ECG-LENS consistently outperforms state-of-the-art methods, with absolute gains of 4.0%, 6.3%, and 11.5% in METEOR, ROUGE-L, and F1-ECGBERT, respectively, over the strongest baselines.

Problem

• ECG report generation
• multi-lead ECG
• clinical report automation
• cardiovascular diagnosis
• diagnostic reasoning

Innovation

• lead-aware modeling
• clinical context enrichment
• diagnosis-aware representation
• ECG-specific evaluation metric
• end-to-end report generation