标题： 掩码自编码器感受心脏：揭示心电图分析中的简单性偏差 显示英文标题

标题： Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses

摘要： 心电图（ECG）的诊断价值在于其动态特性，从节律波动到随时间域和频率域演变的细微波形变形。然而，监督ECG模型往往过度拟合主导且重复的模式，忽视了细粒度但临床上关键的提示，这种现象称为简单性偏差（SB），其中模型更倾向于学习容易获取的信号而非细微但有信息量的信号。在本工作中，我们首先通过实证证明了ECG分析中SB的存在及其对诊断性能的负面影响，同时发现自监督学习（SSL）可以缓解这一问题，为解决偏差提供了一个有前景的方向。遵循SSL范式，我们提出了一种新方法，包含两个关键组件：1）时间-频率感知滤波器，用于捕捉反映ECG信号动态特性的时频特征，以及2）在此基础上，构建多粒度原型重构，以在双域中进行粗粒度和细粒度表示学习，进一步减轻SB。为了推进ECG分析中的SSL，我们整理了一个大规模多中心ECG数据集，包含来自300多个临床中心的153万条记录。在六个ECG数据集上的三个下游任务实验表明，我们的方法有效减少了SB，并实现了最先进性能。 显示英文摘要

摘要： The diagnostic value of electrocardiogram (ECG) lies in its dynamic characteristics, ranging from rhythm fluctuations to subtle waveform deformations that evolve across time and frequency domains. However, supervised ECG models tend to overfit dominant and repetitive patterns, overlooking fine-grained but clinically critical cues, a phenomenon known as Simplicity Bias (SB), where models favor easily learnable signals over subtle but informative ones. In this work, we first empirically demonstrate the presence of SB in ECG analyses and its negative impact on diagnostic performance, while simultaneously discovering that self-supervised learning (SSL) can alleviate it, providing a promising direction for tackling the bias. Following the SSL paradigm, we propose a novel method comprising two key components: 1) Temporal-Frequency aware Filters to capture temporal-frequency features reflecting the dynamic characteristics of ECG signals, and 2) building on this, Multi-Grained Prototype Reconstruction for coarse and fine representation learning across dual domains, further mitigating SB. To advance SSL in ECG analyses, we curate a large-scale multi-site ECG dataset with 1.53 million recordings from over 300 clinical centers. Experiments on three downstream tasks across six ECG datasets demonstrate that our method effectively reduces SB and achieves state-of-the-art performance.