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={Steven Baldassano and Benjamin H. Brinkmann and Hoameng Ung and Tyler Blevins and Erin C. Conrad and Kent Leyde and Mark J. Cook and Ankit N. Khambhati and Joost B. M. Wagenaar and Gregory A. Worrell and Brian Litt},
  journal={Brain : a journal of neurology},
  year={2017},
  volume={140 6},
  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… CONTINUE READING
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  • S. N. Baldassano
  • 2018

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