Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training. However, detecting anomalies in ECG can be challenging due to significant inter-individual differences and anomalies present in both global rhythm and local morphology. To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics. The proposed framework employs a two-branch autoencoder to facilitate multi-scale feature learning through a masking and restoration process, with one branch focusing on global features from the entire ECG and the other on local features from heartbeat-level details, mimicking the diagnostic process of cardiologists. Anomalies are identified by their high restoration errors. To evaluate the performance on a large number of individuals, this paper introduces a new challenging benchmark with signal point-level ground truths annotated by experienced cardiologists. The proposed method demonstrates state-of-the-art performance on this benchmark and two other well-known ECG datasets. The benchmark dataset and source code are available at: \url{https://github.com/MediaBrain-SJTU/ECGAD}

翻译：心电图（ECG）是一种广泛用于检测心脏状况的诊断工具。由于没有训练数据集能够涵盖所有可能的心脏疾病，传统心电图分析可能对罕见心脏疾病存在漏诊。本文提出利用异常检测来识别任何非健康状态，仅使用正常心电图进行训练。然而，由于个体间显著差异以及异常同时存在于整体节律和局部形态中，在心电图中检测异常具有挑战性。为解决这一难题，本文引入了一种新颖的多尺度交叉重构框架，用于心电图异常检测与定位，该框架兼顾心电图的局部与全局特征。所提框架采用双分支自编码器，通过掩码与重构过程实现多尺度特征学习：一个分支专注于整个心电图的全局特征，另一个分支专注于心跳级别细节的局部特征，模拟心脏病专家的诊断流程。异常通过高重构误差进行识别。为评估该方法在大量个体上的性能，本文引入了一个具有挑战性的新基准数据集，其信号点级标注由经验丰富的心脏病专家完成。所提方法在该基准数据集以及另外两个知名心电图数据集上均展现出最先进的性能。基准数据集和源代码可从以下网址获取：\url{https://github.com/MediaBrain-SJTU/ECGAD}