ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation
Electrocardiogram (ECG) is a physiological signal widely used in monitoring heart health, which is of great significance to the detection and diagnosis of heart diseases. Because abnormal heart rhythms are very rare, most ECG datasets have data imbalance problems. At present, many algorithms for ECG...
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doaj-ba89b032d02f49579f523e04dda7cb7a2021-04-29T23:00:46ZengIEEEIEEE Access2169-35362021-01-019520895210010.1109/ACCESS.2021.30698279389779ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram GenerationHaixu Yang0https://orcid.org/0000-0001-9738-2196Jihong Liu1https://orcid.org/0000-0002-0733-4746Lvheng Zhang2Yan Li3Henggui Zhang4College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaInternational Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, ChinaElectrocardiogram (ECG) is a physiological signal widely used in monitoring heart health, which is of great significance to the detection and diagnosis of heart diseases. Because abnormal heart rhythms are very rare, most ECG datasets have data imbalance problems. At present, many algorithms for ECG anomaly automatic recognition are affected by data imbalance. Conventional data augmentation methods are not suitable for the augmentation of the ECG signal, because the ECG signal is one-dimensional and their morphology has physiological significances. In this paper, we propose a ProGAN based ECG sample generation model, called ProEGAN-MS, to solve the problem of data imbalance. The model can stably generate realistic ECG samples. We evaluate the fidelity and diversity of the data generated by the model and compare the data distribution of the original and generated data. In addition, in order to show the diversity of the generated ECG data more intuitively, we manually checked the diversity and calculate the statistics of the data. The results show that compared with other ECG augmentation methods based on GANs, the ECG data generated by our model has higher fidelity and diversity, and the distribution of generated samples is closer to the distribution of original data. Finally, we established neural network models for arrhythmia classification, and used them to evaluate the improvement of the classification model performance by ProEGAN-MS. The results show that augmented data by ProEGAN-MS can effectively improve the insufficient sensitivity and precision of the classification model.https://ieeexplore.ieee.org/document/9389779/Generative adversarial networksdata augmentationelectrocardiogram signalsECG generation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haixu Yang Jihong Liu Lvheng Zhang Yan Li Henggui Zhang |
spellingShingle |
Haixu Yang Jihong Liu Lvheng Zhang Yan Li Henggui Zhang ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation IEEE Access Generative adversarial networks data augmentation electrocardiogram signals ECG generation |
author_facet |
Haixu Yang Jihong Liu Lvheng Zhang Yan Li Henggui Zhang |
author_sort |
Haixu Yang |
title |
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation |
title_short |
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation |
title_full |
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation |
title_fullStr |
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation |
title_full_unstemmed |
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation |
title_sort |
proegan-ms: a progressive growing generative adversarial networks for electrocardiogram generation |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Electrocardiogram (ECG) is a physiological signal widely used in monitoring heart health, which is of great significance to the detection and diagnosis of heart diseases. Because abnormal heart rhythms are very rare, most ECG datasets have data imbalance problems. At present, many algorithms for ECG anomaly automatic recognition are affected by data imbalance. Conventional data augmentation methods are not suitable for the augmentation of the ECG signal, because the ECG signal is one-dimensional and their morphology has physiological significances. In this paper, we propose a ProGAN based ECG sample generation model, called ProEGAN-MS, to solve the problem of data imbalance. The model can stably generate realistic ECG samples. We evaluate the fidelity and diversity of the data generated by the model and compare the data distribution of the original and generated data. In addition, in order to show the diversity of the generated ECG data more intuitively, we manually checked the diversity and calculate the statistics of the data. The results show that compared with other ECG augmentation methods based on GANs, the ECG data generated by our model has higher fidelity and diversity, and the distribution of generated samples is closer to the distribution of original data. Finally, we established neural network models for arrhythmia classification, and used them to evaluate the improvement of the classification model performance by ProEGAN-MS. The results show that augmented data by ProEGAN-MS can effectively improve the insufficient sensitivity and precision of the classification model. |
topic |
Generative adversarial networks data augmentation electrocardiogram signals ECG generation |
url |
https://ieeexplore.ieee.org/document/9389779/ |
work_keys_str_mv |
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