@article {kanters2021deep,
	title = {Deep learning neural network can measure ECG intervals and amplitudes accurately},
	journal = {Journal of Electrocardiology},
	volume = {69},
	year = {2021},
	pages = {82},
	publisher = {Elsevier},
	author = {Jorgen K {Kanters} and Hicks, Steven and Jonas L {Isaksen} and Grarup, Niels and Niels-Henrik Holstein {Rathlou} and Ghouse, Jonas and Ahlberg, Gustav and Morten Salling {Olesen} and Linneberg, Allan and Ellervik, Christina and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {Thambawita2021.04.27.21256189,
	title = {DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine},
	journal = {Scientific Reports},
	year = {2021},
	publisher = {Nature Publishing Group},
	abstract = {Recent global developments underscore the prominent role big data have in modern medical science. Privacy issues are a prevalent problem for collecting and sharing data between researchers. Synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue.In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs.In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by addressing the relevant privacy issues in medical datasets.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialN/AFunding StatementThis work is funded in part by Novo Nordisk Foundation project number NNF18CC0034900.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:We confirm that all experiments were performed in accordance with Helsinki guidelines and regulations of the Danish Regional Committees for Medical and Health Research Ethics. The data studies were approved by the ethical committee of Region Zealand (SJ-113, SJ-114, SJ-191), ethical committee of Copenhagen Amt (KA 98 155).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe Normal DeepFake ECGs are available at OSF (https://osf.io/6hved/) with corresponding MUSE 12SL ground truth values freely downloadable and usable for ECG algorithm development. The DeepFake generative model is available at https://pypi.org/project/deepfake-ecg/ to generate only synthetic ECGs. https://osf.io/6hved/ https://pypi.org/project/deepfake-ecg/},
	doi = {10.1101/2021.04.27.21256189},
	url = {https://www.medrxiv.org/content/early/2021/05/10/2021.04.27.21256189.1},
	author = {Thambawita, Vajira and Jonas L. {Isaksen} and Hicks, Steven and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Morten Salling {Olesen} and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Str{\"u}mke, Inga and Hugo Lewi {Hammer} and Maleckar, Molly and Halvorsen, P{\r a}l and Riegler, Michael and J{\o}rgen K. {Kanters}}
}
@article {27866,
	title = {Explaining deep neural networks for knowledge discovery in electrocardiogram analysis},
	journal = {Scientific Reports},
	volume = {11},
	year = {2021},
	month = {05/2021},
	pages = {10949},
	publisher = {Springer Nature},
	abstract = {Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.},
	doi = {10.1038/s41598-021-90285-5},
	url = {http://www.nature.com/articles/s41598-021-90285-5h},
	author = {Hicks, Steven and Jonas L. {Isaksen} and Thambawita, Vajira and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Str{\"u}mke, Inga and Ellervik, Christina and Morten Salling {Olesen} and Hansen, Torben and Graff, Claus and Holstein-Rathlou, Niels-Henrik and Halvorsen, P{\r a}l and Maleckar, Molly and Riegler, Michael and J{\o}rgen K. {Kanters}}
}
