BenchECG and xECG: a benchmark and baseline for ECG foundation models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915491809853440 |
|---|---|
| author | Lunelli, Riccardo Nicolson, Angus Pröll, Samuel Martin Reinstadler, Sebastian Johannes Bauer, Axel Dlaska, Clemens |
| author_facet | Lunelli, Riccardo Nicolson, Angus Pröll, Samuel Martin Reinstadler, Sebastian Johannes Bauer, Axel Dlaska, Clemens |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BenchECG and xECG: a benchmark and baseline for ECG foundation models Lunelli, Riccardo Nicolson, Angus Pröll, Samuel Martin Reinstadler, Sebastian Johannes Bauer, Axel Dlaska, Clemens Machine Learning Artificial Intelligence I.2.1 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. |
| title | BenchECG and xECG: a benchmark and baseline for ECG foundation models |
| topic | Machine Learning Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2509.10151 |