标题： TolerantECG：一种用于不完美心电图的基础模型 显示英文标题

标题： TolerantECG: A Foundation Model for Imperfect Electrocardiogram

摘要： 心电图（ECG）是诊断心脏病的重要且有效的工具。 然而，由于噪声或标准12导联记录中一个或多个导联不可用，其有效性可能会受到影响，从而导致诊断错误或不确定性。 为解决这些挑战，我们提出了TolerantECG，这是一种针对ECG信号的基础模型，能够抵御噪声，并能在任意子集的标准12导联ECG下正常工作。 TolerantECG的训练结合了对比学习和自监督学习框架，共同学习ECG信号表示及其对应的基于知识检索的文本报告描述以及损坏或导联缺失的信号。 全面的基准测试结果表明， 在PTB-XL数据集中，TolerantECG在各种ECG信号条件和类别级别上始终排名最佳或第二佳，并在MIT-BIH心律失常数据库上取得了最高性能。 显示英文摘要

摘要： The electrocardiogram (ECG) is an essential and effective tool for diagnosing heart diseases. However, its effectiveness can be compromised by noise or unavailability of one or more leads of the standard 12-lead recordings, resulting in diagnostic errors or uncertainty. To address these challenges, we propose TolerantECG, a foundation model for ECG signals that is robust to noise and capable of functioning with arbitrary subsets of the standard 12-lead ECG. TolerantECG training combines contrastive and self-supervised learning frameworks to jointly learn ECG signal representations alongside their corresponding knowledge-retrieval-based text report descriptions and corrupted or lead-missing signals. Comprehensive benchmarking results demonstrate that TolerantECG consistently ranks as the best or second-best performer across various ECG signal conditions and class levels in the PTB-XL dataset, and achieves the highest performance on the MIT-BIH Arrhythmia Database.