  The electrocardiogram (ECG) is an essential tool for diagnosing heart
disease, with computer-aided systems improving diagnostic accuracy and reducing
healthcare costs. Despite advancements, existing systems often miss rare
cardiac anomalies that could be precursors to serious, life-threatening issues
or alterations in the cardiac macro/microstructure. We address this gap by
focusing on self-supervised anomaly detection (AD), training exclusively on
normal ECGs to recognize deviations indicating anomalies. We introduce a novel
self-supervised learning framework for ECG AD, utilizing a vast dataset of
normal ECGs to autonomously detect and localize cardiac anomalies. It proposes
a novel masking and restoration technique alongside a multi-scale
cross-attention module, enhancing the model's ability to integrate global and
local signal features. The framework emphasizes accurate localization of
anomalies within ECG signals, ensuring the method's clinical relevance and
reliability. To reduce the impact of individual variability, the approach
further incorporates crucial patient-specific information from ECG reports,
such as age and gender, thus enabling accurate identification of a broad
spectrum of cardiac anomalies, including rare ones. Utilizing an extensive
dataset of 478,803 ECG graphic reports from real-world clinical practice, our
method has demonstrated exceptional effectiveness in AD across all tested
conditions, regardless of their frequency of occurrence, significantly
outperforming existing models. It achieved superior performance metrics,
including an AUROC of 91.2%, an F1 score of 83.7%, a sensitivity rate of 84.2%,
a specificity of 83.0%, and a precision of 75.6% with a fixed recall rate of
90%. It has also demonstrated robust localization capabilities, with an AUROC
of 76.5% and a Dice coefficient of 65.3% for anomaly localization.
