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

TL;DR - ECG-LENS is an end-to-end framework that turns multi-lead ECG signals into clinical-grade text reports, paired with a new ECG-specific evaluation metric. It matters because most prior work stops at classification, leaving generated reports too weak for real clinical use.

• Architecture combines lead-wise encoders (preserving localized waveform morphology) with a global encoder for inter-lead dependencies; fused signal representations plus clinically enriched textual prompts condition a GPT-2 decoder.
• Adds an ECG-specific report-preprocessing strategy to steer the model toward clinically meaningful findings rather than boilerplate text.
• Proposes F1-ECGBERT, a BERT-based metric scoring agreement between diagnostic labels extracted from generated vs. reference reports, addressing the known bias of lexical metrics like BLEU/ROUGE.
• Evaluated in-domain on PTB-XL and cross-domain on MIMIC-IV-ECG, reporting absolute gains of 4.0% METEOR, 6.3% ROUGE-L, and 11.5% F1-ECGBERT over the strongest baselines.