Abstract：
Cardiac abnormality detection from Electrocardiogram (ECG) signals is a common task for cardiologists. To facilitate efficient and objective detection, automated ECG classification by using deep learning based methods have been developed in recent years. Despite their impressive performance, these methods perform poorly when presented with cardiac abnormalities that are not well represented, or absent, in the training data. To this end, we propose a novel one-class classification based ECG anomaly detection generative adversarial network (GAN). Specifically, we embedded a Bi-directional Long-Short Term Memory (Bi-LSTM) layer into a GAN architecture and used a mini-batch discrimination training strategy in the discriminator to synthesis ECG signals. Our method generates samples to match the data distribution from normal signals of healthy group so that a generalised anomaly detector can be built reliably. The experimental results demonstrate our method outperforms several state-of-the-art semi-supervised learning based ECG anomaly detection algorithms and robustly detects the unknown anomaly class in the MIT-BIH arrhythmia database. Experiments show that our method achieves the accuracy of 95.5% and AUC of 95.9% which outperforms the most competitive baseline by 0.7% and 1.7% respectively. Our method may prove to be a helpful diagnostic method for helping cardiologists identify arrhythmias.
摘要：
从心电图(ECG)信号检测心脏异常是心脏病学家的常见任务。为了便于高效客观的检测，近年来，已经开发了使用基于深度学习的方法进行自动ECG分类。尽管他们的表现令人印象深刻，当出现心脏异常时，这些方法表现不佳，或缺席,在训练数据中。为此,我们提出了一种新颖的基于一类分类的ECG异常检测生成对抗网络（GAN）。具体来说,我们将双向长短期记忆（Bi-LSTM）层嵌入到GAN架构中，并在鉴别器中使用小批量辨别训练策略来合成ECG信号。我们的方法生成样本以匹配健康组的正常信号的数据分布，以便可以可靠地构建广义异常检测器。实验结果表明，我们的方法优于几种最新的基于半监督学习的ECG异常检测算法，并且可以鲁棒地检测MIT-BIH心律失常数据库中的未知异常类别。实验表明，我们的方法达到了95.5%的准确率和95.9%的AUC，分别优于最有竞争力的基线0.7%和1.7%。我们的方法可能被证明是帮助心脏病专家识别心律失常的有用诊断方法。
