Cardiologists use electrocardiograms (ECG) for the detection of arrhythmias.
However, continuous monitoring of ECG signals to detect cardiac abnormal-ities
requires significant time and human resources. As a result, several deep
learning studies have been conducted in advance for the automatic detection of
arrhythmia. These models show relatively high performance in supervised
learning, but are not applicable in cases with few training examples. This is
because abnormal ECG data is scarce compared to normal data in most real-world
clinical settings. Therefore, in this study, GAN-based anomaly detec-tion,
i.e., unsupervised learning, was employed to address the issue of data
imbalance. This paper focuses on detecting abnormal signals in
electrocardi-ograms (ECGs) using only labels from normal signals as training
data. In-spired by self-supervised vision transformers, which learn by dividing
images into patches, and masked auto-encoders, known for their effectiveness in
patch reconstruction and solving information redundancy, we introduce the ECG
Heartbeat Anomaly Detection model, EB-GAME. EB-GAME was trained and validated
on the MIT-BIH Arrhythmia Dataset, where it achieved state-of-the-art
performance on this benchmark.

该研究使用深度学习基于生成对抗网络的异常检测模型（EB-GAME）在心电图中仅使用正常信号标签的训练数据进行异常信号检测，并在 MIT-BIH 心律失常数据集上取得了最先进的性能表现。