Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite these efforts, performance remains suboptimal. A key obstacle is the inherent complexity of ECG data, which includes heterogeneity (e.g., varying sampling rates), high levels of noise, demographic-related pattern shifts, and intricate rhythm-event associations. To overcome these challenges, this paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data. Specifically, a tailored ECG Tokenizer encodes each fixed-duration ECG fragment into a token and, guided by proxy tasks, converts noisy, continuous ECG features into discrete, compact, and clinically meaningful local rhythm codes. These codes encapsulate basic morphological, frequency, and demographic information (e.g., sex), effectively mitigating signal noise. We further pre-train the AnyECG to learn rhythmic pattern associations across ECG tokens, enabling the capture of cardiac event semantics. By being jointly pre-trained on diverse ECG data sources, AnyECG is capable of generalizing across a wide range of downstream tasks where ECG signals are recorded from various devices and scenarios. The experimental results show that AnyECG achieves an average performance improvement of 6% across four critical tasks-anomaly detection, arrhythmia classification, corrupted lead generation, and ultra-long ECG recognition. AnyECG learns common ECG rhythm from data and significantly outperforms state-of-the-art methods in each of these tasks.

翻译：心电图（ECG）作为一种无创且经济的心脏监测工具，在急性心脏病发作检测中具有高度敏感性。然而，由于心电图记录通常较长，为减轻人工负担，已开发出多种用于自动化心脏病检测的机器学习方法。尽管已有这些努力，其性能仍不尽如人意。一个关键障碍在于心电图数据固有的复杂性，包括异质性（如采样率不同）、高噪声水平、与人口统计学相关的模式偏移以及复杂的节律-事件关联。为克服这些挑战，本文提出AnyECG，一种旨在从任何真实世界心电图数据中提取鲁棒表征的基础模型。具体而言，一个定制化的ECG Tokenizer将每个固定时长的心电图片段编码为一个令牌，并在代理任务的引导下，将含噪声的连续心电图特征转换为离散、紧凑且具有临床意义的局部节律代码。这些代码封装了基本的形态学、频率及人口统计学信息（如性别），有效缓解了信号噪声。我们进一步对AnyECG进行预训练，以学习跨心电图令牌的节律模式关联，从而捕捉心脏事件语义。通过在多样化的心电图数据源上进行联合预训练，AnyECG能够泛化至广泛的下游任务，这些任务中的心电图信号采集自不同设备与场景。实验结果表明，在异常检测、心律失常分类、导联损坏生成及超长心电图识别四项关键任务中，AnyECG平均性能提升达6%。AnyECG从数据中学习通用的心电图节律模式，并在每项任务中显著优于现有最优方法。