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A Novel Temporal Generative Adversarial Network for Electrocardiography Anomaly Detection.pdf (1.21 MB)

A novel temporal generative adversarial network for electrocardiography anomaly detection

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journal contribution
posted on 2023-01-11, 10:55 authored by Jing Qin, Fujie Gao, Zumin Wang, David Wong, Zhibin Zhao, Samuel Relton, Hui FangHui Fang

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 learn ing 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.

Funding

Youth Fund Project of the National Nature Fund of China under Grant 62002038

History

School

  • Science

Department

  • Computer Science

Published in

Artificial Intelligence in Medicine

Volume

136

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Artificial Intelligence in Medicine and the definitive published version is available at https://doi.org/10.1016/j.artmed.2023.102489

Acceptance date

2023-01-09

Publication date

2023-01-13

Copyright date

2023

ISSN

0933-3657

eISSN

1873-2860

Language

  • en

Depositor

Dr Hui Fang. Deposit date: 10 January 2023

Article number

102489

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