{"ID":2870392,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12991","arxiv_id":"2509.12991","title":"Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy","abstract":"ECG foundation models are increasingly popular due to their adaptability across various tasks. However, their clinical applicability is often limited by performance gaps compared to task-specific models, even after pre-training on large ECG datasets and fine-tuning on target data. This limitation is likely due to the lack of an effective post-training strategy. In this paper, we propose a simple yet effective post-training approach to enhance ECG foundation models. We evaluate it on a publicly available Transformer-based foundation model. Experiments across multiple ECG tasks show that our method consistently outperforms baseline fine-tuning. On the PTB-XL benchmarks, it improves macro AUROC by 0.7%-8.9% and macro AUPRC by 23.3%-77.9%, also outperforming several recent state-of-the-art approaches, including task-specific and advanced architectures. Further analyses demonstrate improved training dynamics and data efficiency, with only 30% of the training data outperforming the baseline trained on the full dataset. Ablation studies highlight the importance of stochastic depth and preview linear probing. These findings underscore the potential of post-training strategies to improve ECG foundation models, and we hope this work will contribute to the continued development of foundation models in the ECG domain.","short_abstract":"ECG foundation models are increasingly popular due to their adaptability across various tasks. However, their clinical applicability is often limited by performance gaps compared to task-specific models, even after pre-training on large ECG datasets and fine-tuning on target data. This limitation is likely due to the l...","url_abs":"https://arxiv.org/abs/2509.12991","url_pdf":"https://arxiv.org/pdf/2509.12991v2","authors":"[\"Ya Zhou\",\"Yujie Yang\",\"Xiaohan Fan\",\"Wei Zhao\"]","published":"2025-09-16T12:02:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.AP\"]","methods":"[\"Transformer\"]","has_code":false}