Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings

@article{Baldassano2017CrowdsourcingSD,
  title={Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings},
  author={S. Baldassano and B. Brinkmann and H. Ung and Tyler Blevins and E. Conrad and K. Leyde and M. Cook and A. Khambhati and Joost B. Wagenaar and G. Worrell and B. Litt},
  journal={Brain},
  year={2017},
  volume={140},
  pages={1680–1691}
}
There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle… Expand
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