Crowdsourcing reproducible seizure forecasting in human and canine epilepsy
Por:
Brinkmann BH, Wagenaar J, Abbot D, Adkins P, Bosshard SC, Chen M, Tieng QM, He J, Muñoz-Almaraz FJ, Botella-Rocamora P, Pardo J, Zamora-Martinez F, Hills M, Wu W, Korshunova I, Cukierski W, Vite C, Patterson EE, Litt B and Worrell GA
Publicada:
1 jun 2016
Resumen:
Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance.See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. aEuro,
Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance.Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 +/- 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.
Filiaciones:
Brinkmann BH:
Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
Wagenaar J:
University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA
Abbot D:
AiLive Inc, Sunnyvale, CA, USA
Adkins P:
AiLive Inc, Sunnyvale, CA, USA
Bosshard SC:
University of Queensland, Centre for Advanced Imaging, Queensland, Australia
Chen M:
University of Queensland, Centre for Advanced Imaging, Queensland, Australia
Tieng QM:
University of Queensland, Centre for Advanced Imaging, Queensland, Australia
He J:
Hemedics Inc, Boston, MA, USA
Muñoz-Almaraz FJ:
CEU Cardenal Herrera University, Valencia, Spain
:
CEU Cardenal Herrera University, Valencia, Spain
Pardo J:
CEU Cardenal Herrera University, Valencia, Spain
Zamora-Martinez F:
CEU Cardenal Herrera University, Valencia, Spain
Hills M:
Sydney, Australia
Wu W:
New York, NY, USA
Korshunova I:
Ghent University, Ghent, Belgium
Cukierski W:
Kaggle, Inc. New York NY, USA
Vite C:
University of Pennsylvania, School of Veterinary Medicine Philadelphia, PA, USA
Patterson EE:
University of Minnesota, Veterinary Medical Center, St. Paul, MN, USA
Litt B:
University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA
Worrell GA:
Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
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