Abnormal ECG detection based on an adversarial autoencoder

Lianfeng Shan,Yu Li,Hua Jiang, Peng Zhou, Jing Niu, Ran Liu,Yuanyuan Wei, Jiao Peng, Huizhen Yu,Xianzheng Sha,Shijie Chang

FRONTIERS IN PHYSIOLOGY(2022)

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摘要
Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.
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关键词
outlier detection (OD), autoencoder (AE), generative adversarial network (GANs), ECG, temporal convolutional network (TCN)
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