ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network

Wang, Pu; Hou, Borui; Shao, Siyu; Yan, Ruqiang

doi:10.1109/access.2019.2930882

Cited by 64 publications

(38 citation statements)

References 24 publications

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“…There are few studies on using GAN to generate ECG signals. Wang et al [34] were the first to use GAN to gen-erate ECG signals to solve data imbalance. They construct a data augmentation model based on an auxiliary classifier generative adversarial network (ACGAN) [38].…”

Section: Related Workmentioning

confidence: 99%

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ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%

“…However, there are very few studies [21,28,34,35] on ECG signal generation using GAN, and their quality assessment of the generated ECG signal is not comprehensive. The above GAN-based ECG generation studies have different evaluation indicators.…”

Section: Introductionmentioning

confidence: 99%

ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation

et al. 2021

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“…Some researchers [ 5 – 8 ] employed convolutional neural networks (CNNs), which automatically extract the ECG features and significantly improve the final prediction. Some works [ 9 , 10 ] proposed a deep learning architecture based on a convolutional recurrent neural network (GRNN) to detect arrhythmias. Li et al [ 11 ] designed the architecture of the deep neural network, CraftNet, for accurately recognizing the features, and assembled multiple child classifiers to classify heartbeats.…”

Section: Related Workmentioning

confidence: 99%

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Shen

et al. 2021

Journal of Healthcare Engineering

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Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

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“…ACGAN is created by adding multiple 1-dimensional convolutional layers to the Discriminator and Generator. In experimental studies, the model was applied for sequential heartbeat detection and single-heartbeat detection [54]. García, Laparra and Chova (2019) present the use of a GAN framework to generate new satellite data in their paper.…”

Section: Work With Generative Adversarial Networkmentioning

confidence: 99%

Generative Networks and Royalty-Free Products

Özkan

Erdoğmuş

2020

Sakarya University Journal of Computer and Information Sciences

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In recent years, with the increasing power of computers and Graphics Processing Units (GPUs), vast variety of deep neural networks architectures have been created and realized. One of the most interesting and generative type of the networks are Generative Adversarial Networks (GANs). GANs are used to create things such as music, images or a film scenerio. GANs consist of two networks working simultaneously. Generative network captures data distribution and discriminative network estimates the probability of the Generative Network output, coming from training data of discriminative network. The objective is to both maximizing the generative network products reality and minimize the discriminative network classification error. This procedure is a minimax two-player game. In this paper, it has been aimed to review the latest studies with GANs, to gather the recent studies in an article and to discuss the possible issues with royalty free products created by GANs. With this aim, from 2018 to today, the studies on GANs have been gathered to the citation numbers. As a result, the recent studies with GANs have been summarized and the potential issues related to GANs have been submitted.

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ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network

Cited by 64 publications

References 24 publications

ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation

ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm

Generative Networks and Royalty-Free Products

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