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1from transformers import AutoModel
2
3model = AutoModel.from_pretrained("deepsynthbody/deepfake_ecg", trust_remote_code=True)
4
5out = model(num_samples=5)lead III value = (lead II value) - (lead I value)
lead aVR value = -0.5*(lead I value + lead II value)
lead aVL value = lead I value - 0.5 * lead II value
lead aVF value = lead II value - 0.5 * lead I value
- In this repository, there are two DeepFake datasets:
1. 150k dataset - Randomly generated 150k DeepFakeECGs
2. Filtered all normals dataset - Only "Normal" ECGs filtered using the MUSE analysis report
1@article{thambawita2021deepfake,
2 title={DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
3 author={Thambawita, Vajira and Isaksen, Jonas L and Hicks, Steven A and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and others},
4 journal={Scientific reports},
5 volume={11},
6 number={1},
7 pages={1--8},
8 year={2021},
9 publisher={Nature Publishing Group}
10}