NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest

NeuroECG is a deep learning framework that repurposes a pretrained ECG foundation model to predict neurological outcomes after cardiac arrest without using electroencephalography (EEG). The model fine‑tunes the backbone with a gradual unfreezing strategy on single‑channel bedside ECG, aggregates multiple ECG segments via quantile pooling and PCA, and achieves an AUROC of 0.7333 using ECG alone. When combined with static clinical covariates, NeuroECG improves performance to an AUROC of 0.8077 and an AUPRC of 0.8970, demonstrating that bedside ECG can serve as a low‑cost, auxiliary prognostic tool in an EEG‑free setting.

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• rag
• fine-tuning