Journal article

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

Abstract

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..

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University of Melbourne Researchers

Grants

Awarded by National Institutes of Health (NIH)


Awarded by NIH


Awarded by NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE


Funding Acknowledgements

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).