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2019

Quick and Easy Time Series Generation with Established Image-based GANs

Brophy, Eoin, Wang, Zhengwei, Ward, Tomas E.

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In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data.

In this work we introduce our simple method to exploit the advancements in well established image-based GANs to synthesise single channel time series data.
We implement Wasserstein GANs (WGANs) with gradient penalty due to their stability in training to synthesise three different types of data; sinusoidal data, photoplethysmograph (PPG) data and electrocardiograph (ECG) data.
The length of the returned time series data is limited only by the image resolution, we use an image size of 64x64 pixels which yields 4096 data points.

Quick and Easy Time Series Generation with Established Image-based GANs · Around

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Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101
2000
Earlier work this paper cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017). https://doi.org/10.2507/daaam.scibook.2010.27, http://arxiv.org/abs/1701.07875
Original
2010
Earlier work this paper cites.
Garcia-Gonzalez, M., Argelagós, A., Fernández-Chimeno, M., Ramos-Castro, J.: Differences in qrs locations due to ecg lead: relationship with breathing. In: XIII Mediteranean Conference on Medical and Biological Engineering and Computing 2013. pp. 962–964. Springer, Cham (2014)
2014
Earlier work this paper cites.

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Pimentel, M.A., Johnson, A.E., Charlton, P.H., Birrenkott, D., Watkinson, P.J., Tarassenko, L., Clifton, D.A.: Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Transactions on Biomedical Engineering 64
Original
2016
Cited alongside, same era.
Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs (2017). https://doi.org/10.1002/fut, http://arxiv.org/abs/1706.02633
Original
2017
Cited alongside, same era.

Then

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 5767–5777. Curran Associates, Inc. (2017), http://papers.nips.cc/paper/7159-improved-training-of-wasserstein-gans.pdf
2017
Later among the works it cites.
Hartmann, K.G., Schirrmeister, R.T., Ball, T.: EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals (2018), http://arxiv.org/abs/1806.01875
Original
2018
Later among the works it cites.

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