BenchECG and xECG: a benchmark and baseline for ECG foundation models
This work addresses the problem of inconsistent evaluation in ECG foundation models for researchers, providing a benchmark and baseline to accelerate progress, though it is incremental in improving standardization.
The authors tackled the lack of consistent evaluation for ECG foundation models by introducing BenchECG, a standardized benchmark with comprehensive datasets and tasks, and xECG, an xLSTM-based model that achieved the best BenchECG score and performed strongly across all datasets and tasks.
Electrocardiograms (ECGs) are inexpensive, widely used, and well-suited to deep learning. Recently, interest has grown in developing foundation models for ECGs - models that generalise across diverse downstream tasks. However, consistent evaluation has been lacking: prior work often uses narrow task selections and inconsistent datasets, hindering fair comparison. Here, we introduce BenchECG, a standardised benchmark comprising a comprehensive suite of publicly available ECG datasets and versatile tasks. We also propose xECG, an xLSTM-based recurrent model trained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score compared to publicly available state-of-the-art models. In particular, xECG is the only publicly available model to perform strongly on all datasets and tasks. By standardising evaluation, BenchECG enables rigorous comparison and aims to accelerate progress in ECG representation learning. xECG achieves superior performance over earlier approaches, defining a new baseline for future ECG foundation models.