Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources.
Bedside electrocardiography (ECG) is standard and low-cost. Yet, its value for predicting neurological outcomes remains underexplored.
In this study
We propose NeuroECG, an ECGFounder-based deep representation framework for EEG-free auxiliary prognostication.
NeuroECG adapts a pretrained ECG foundation model via task-specific fine-tuning.
We implement a gradual unfreezing strategy on single-channel bedside monitoring ECG.
Multiple ECG segments per patient are encoded into segment-level deep features.
These embeddings are aggregated via quantile pooling ($q = 0.24$) and compressed using principal component analysis (PCA).
Experiments
Experiments on 412 ECG-available patients from the multicenter I-CARE database show that the adapted ECGFounder backbone achieves the best performance among ECG-only backbone baselines, with a test AUROC of 0.7333.
We further combine the learned deep ECG representation with static clinical covariates.
The proposed NeuroECG model achieves a test AUROC of 0.8077 and an AUPRC of 0.8970.
These results support deep bedside ECG representations as a useful source of auxiliary prognostic information.
Their integration with static clinical covariates improves prediction in an EEG-free setting.
The source code is available at https://github.com/goddream66/NeuroECG_