Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings
Steven N Baldassano, Benjamin H Brinkmann, Hoameng Ung, Tyler Blevins, Erin C Conrad, Kent Leyde, Mark J Cook, Ankit N Khambhati, Joost B Wagenaar, Gregory A Worrell, Brian Litt
BRAIN | OXFORD UNIV PRESS | Published : 2017
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.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from th..View full abstract
Awarded by National Institutes of Health (NIH)
Awarded by NIH
Awarded by NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
This research was supported by the National Institutes of Health (NIH) (UH2-NS095495-01, R01NS092882, 1K01ES025436-01), the Mirowski Family Foundation, the Ashton Fellowship at the University of Pennsylvania, and contributions from Neil and Barbara Smit. The International Epilepsy Electrophysiology (IEEG) Portal is funded by the NIH (5-U24-NS-063930-05).