PIMGAN: Prior-induced Masked GAN for Myocardial Infarction Anomaly Detection

Myocardial infarction (MI) is a critical heart disease that occurs when the blood supply to the coronary arteries is drastically reduced or interrupted. Deep learning models of MI detection from electrocardiogram (ECG) play an important role in disease early diagnosis. However, most existing methods rely on numerous labeled training samples of MI ECG, which are scarce data in real scenarios. In this paper, we model MI detection as an anomaly detection problem that does not require any MI ECG samples for training. Moreover, inspired by the diagnostic prior knowledge from electrocardiologists, we propose a simple and effective method of unsupervised anomaly detection called PIMGAN, which imposes the model to focus on essential wave features of MI ECG such as Q wave, T wave, and ST segment. Specifically, PIMGAN is a prior-induced mask-based dual generative adversarial network (GAN), where one GAN learns the global feature from the original ECG, and another GAN learns the key MI wave feature from the masked ECG. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of the proposed method compared with the current advanced anomaly detection baselines.