Fetching the paper…

Reading the bibliography…

2019

Synthesis of Realistic ECG using Generative Adversarial Networks

Delaney, Anne Marie, Brophy, Eoin, Ward, Tomas E.

Original
PDF
Post on X

Understand

Access to medical data is highly restricted due to its sensitive nature, preventing communities from using this data for research or clinical training.

Common methods of de-identification implemented to enable the sharing of data are sometimes inadequate to protect the individuals contained in the data.
For our research, we investigate the ability of generative adversarial networks (GANs) to produce realistic medical time series data which can be used without concerns over privacy.
The aim is to generate synthetic ECG signals representative of normal ECG waveforms.

Synthesis of Realistic ECG using Generative Adversarial Networks · Around

Built on

S. Hochreiter and J. Urgen Schmidhuber, “Lstm,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997. [Online]. Available: http://www7.informatik.tu-muenchen.de/{˜}hochreit{%}0Ahttp://www.idsia.ch/{˜}juergen
1997
Earlier work this paper cites.
M. Schuster and K. K. Paliwal, “Bidirectional Recurrent Neural Networks,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, , vol. 45, no. 11, pp. 1–10, 1997. [Online]. Available: https://pdfs.semanticscholar.org/4b80/89bc9b49f84de43acc2eb8900035f7d492b2.pdf
1997
Earlier work this paper cites.
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation , vol. 101, no. 23, pp. e215–e220, 2000 (June 13), circulation Electronic Pages: http://circ.ahajournals.org/content/101/23/e215.full PMID:1085218; doi: 10.1161/01.CIR.101.23.e215
2000
Earlier work this paper cites.
G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine , vol. 20, no. 3, pp. 45–50, 2001
2001
Earlier work this paper cites.
B. Malin and L. Sweeney, “Re-identification of DNA through an automated linkage process.” Proceedings / AMIA … Annual Symposium. AMIA Symposium , pp. 423–427, 2001
2001
Earlier work this paper cites.
P. E. McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, “A dynamical model for generating synthetic electrocardiogram signals,” IEEE Transactions on Biomedical Engineering , vol. 50, no. 3, pp. 289–294, 2003
2003
Earlier work this paper cites.
S. Salvador and P. Chan, “Toward accurate dynamic time warping in linear time and space,” Intelligent Data Analysis , vol. 11, no. 5, pp. 561–580, 2007
2007
Earlier work this paper cites.
K. El Emam, E. Jonker, L. Arbuckle, and B. Malin, “A systematic review of re-identification attacks on health data,” PLoS ONE , vol. 6, no. 12, 2011
2011
Earlier work this paper cites.
Y. Park and J. Ghosh, “Perturbed Gibbs Samplers for Synthetic Data Release,” arXiv preprint arXiv:1312.5370 , no. 2008, pp. 1–19, 2013. [Online]. Available: http://arxiv.org/abs/1312.5370
Original
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2672–2680. [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” pp. 1–7, 2014. [Online]. Available: http://arxiv.org/abs/1411.1784
Original
2014
Earlier work this paper cites.
J. Serrà and J. L. Arcos, “An empirical evaluation of similarity measures for time series classification,” Knowledge-Based Systems , vol. 67, pp. 305–314, 2014
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” pp. 1–15, 2014. [Online]. Available: http://arxiv.org/abs/1412.6980
Original
2014
Earlier work this paper cites.
K. E. Emam, S. Rodgers, and B. Malin, “Anonymising and sharing individual patient data,” BMJ (Online) , vol. 350, 2015
2015
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” pp. 1–16, 2015. [Online]. Available: http://arxiv.org/abs/1511.06434
Original
2015
Earlier work this paper cites.

Similar

S. McLachlan, K. Dube, and T. Gallagher, “Using the CareMap with Health Incidents Statistics for Generating the Realistic Synthetic Electronic Healthcare Record,” Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 , pp. 439–448, 2016
2016
Cited alongside, same era.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” 33rd International Conference on Machine Learning, ICML 2016 , vol. 3, pp. 1681–1690, 2016
2016
Cited alongside, same era.
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WaveNet: A Generative Model for Raw Audio,” pp. 1–15, 2016. [Online]. Available: http://arxiv.org/abs/1609.03499
Original
2016
Cited alongside, same era.
D. J. Im, C. D. Kim, H. Jiang, and R. Memisevic, “Generating images with recurrent adversarial networks,” 2016. [Online]. Available: http://arxiv.org/abs/1602.05110
Original
2016

Then

E. Choi, S. Biswal, B. Malin, J. Duke, W. F. Stewart, and J. Sun, “Generating Multi-label Discrete Patient Records using Generative Adversarial Networks,” pp. 1–20, 2017. [Online]. Available: http://arxiv.org/abs/1703.06490
Original
2017
Later among the works it cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative Adversarial Networks: An Overview,” IEEE Signal Processing Magazine , vol. 35, no. 1, pp. 53–65, Jan 2018
2018
Later among the works it cites.
European Union, “Data protection act 2018 (section36(2)),” 2018, http://www.irishstatutebook.ie/eli/2018/si/314/made/en/pdf
2018
Later among the works it cites.
C. Donahue, J. McAuley, and M. Puckette, “Adversarial Audio Synthesis,” pp. 1–16, 2018. [Online]. Available: http://arxiv.org/abs/1802.04208
Original
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

O. Mogren, “C-RNN-GAN: Continuous recurrent neural networks with adversarial training,” 2016. [Online]. Available: http://arxiv.org/abs/1611.09904

2016

Cited alongside, same era.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training GANs,” Advances in Neural Information Processing Systems , pp. 2234–2242, 2016

2016

Cited alongside, same era.

M. Abadi, H. B. McMahan, A. Chu, I. Mironov, L. Zhang, I. Goodfellow, and K. Talwar, “Deep learning with differential privacy,” Proceedings of the ACM Conference on Computer and Communications Security , vol. 24-28-October-2016, no. Ccs, pp. 308–318, 2016

2016

Cited alongside, same era.

D. J. Sutherland, H.-Y. Tung, H. Strathmann, S. De, A. Ramdas, A. Smola, and A. Gretton, “Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy,” pp. 1–11, 2016. [Online]. Available: http://arxiv.org/abs/1611.04488

2016

Cited alongside, same era.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , vol. 2017-January, pp. 105–114, 2017

2017

Cited alongside, same era.

C. Culnane, B. I. P. Rubinstein, and V. Teague, “Health Data in an Open World,” 2017. [Online]. Available: http://arxiv.org/abs/1712.05627

2017

Cited alongside, same era.

J. T. Guibas, T. S. Virdi, and P. S. Li, “Synthetic Medical Images from Dual Generative Adversarial Networks,” pp. 1–9, 2017. [Online]. Available: http://arxiv.org/abs/1709.01872

2017

Cited alongside, same era.

L. Yu, W. Zhang, J. Wang, and Y. Yu, “SeqGAN: Sequence generative adversarial nets with policy gradient,” 31st AAAI Conference on Artificial Intelligence, AAAI 2017 , no. Goodfellow, pp. 2852–2858, 2017

2017

Cited alongside, same era.

K. G. Hartmann, R. T. Schirrmeister, and T. Ball, “EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals,” 2018. [Online]. Available: http://arxiv.org/abs/1806.01875

2018

Later among the works it cites.

D. J. Im, H. Ma, G. Taylor, and K. Branson, “Quantitatively Evaluating GANs With Divergences Proposed for Training,” pp. 1–30, 2018. [Online]. Available: http://arxiv.org/abs/1803.01045

2018

Later among the works it cites.

Q. Xu, G. Huang, Y. Yuan, C. Guo, Y. Sun, F. Wu, and K. Weinberger, “An empirical study on evaluation metrics of generative adversarial networks,” 2018. [Online]. Available: http://arxiv.org/abs/1806.07755

2018

Later among the works it cites.

L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou, “Differentially Private Generative Adversarial Network,” 2018. [Online]. Available: http://arxiv.org/abs/1802.06739

2018

Later among the works it cites.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights into Imaging , vol. 9, no. 4, pp. 611–629, Aug 2018. [Online]. Available: https://doi.org/10.1007/s13244-018-0639-9

2018

Later among the works it cites.

M. Kachuee, S. Fazeli, and M. Sarrafzadeh, “ECG heartbeat classification: A deep transferable representation,” Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 , pp. 443–444, 2018

2018

Later among the works it cites.

B. P. Hejblum, G. M. Weber, K. P. Liao, N. P. Palmer, S. Churchill, N. A. Shadick, P. Szolovits, S. N. Murphy, I. S. Kohane, and T. Cai, “Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes,” Scientific Data , vol. 6, pp. 1–11, 2019

2019

Closest in time.

J. Dahmen and D. Cook, “SynSys: A synthetic data generation system for healthcare applications,” Sensors (Switzerland) , vol. 19, no. 5, pp. 1–11, 2019

2019

Closest in time.

F. Zhu, F. Ye, Y. Fu, Q. Liu, and B. Shen, “Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network,” Scientific Reports , vol. 9, no. 1, pp. 1–11, 2019

2019

Closest in time.

A. Borji, “Pros and cons of GAN evaluation measures,” Computer Vision and Image Understanding , vol. 179, pp. 41–65, 2019

2019

Closest in time.

J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “LOGAN: Membership Inference Attacks Against Generative Models,” Proceedings on Privacy Enhancing Technologies , vol. 2019, no. 1, pp. 133–152, 2019

2019

Closest in time.