Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
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. Code and dataset will be released
publicly.
