Terms and Conditions
Terms and conditions are provided for the three datasets below. When you register with the ecosystem you accept all of these terms and conditions by default.
Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge
1. The contest data from the ‘Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge’ that can be downloaded here, and any derivatives of the data, can only be used for non-commercial purposes. Any use of the data, or part of the data, including software development, algorithm testing, or any other use, must be for non-commercial purposes.
2. The data may be used for academic purposes such as student assignments or projects and research purposes such as journal and conference publications, under the constraint that use of the contest data in journal and conference publications requires including Dr Levin Kuhlmann from the University of Melbourne and Swinburne University of Technology as a co-author for preparing the data and benchmarking your algorithm against other algorithms in the ecosystem. Noting this requirement, Dr Kuhlmann reserves the right to decide to not be part of any such publication. Class labels for the training and public test sets from the contest are provided. To submit your complete test set predictions and obtain Area Under the Curve (AUC) performance scores for your algorithm for the private test set please contact Dr Levin Kuhlmann at
Email: levin.kuhlmann@monash.edu
Department of Medicine – St. Vincent’s Hospital Melbourne
St Vincent’s Hospital Melbourne
Clinical Sciences Building,
Level 4 / 29 Regent Street,
Fitzroy VIC 3065, Australia
3. Use of the contest data and/or top algorithms in journal, conference or other publications, and/or source code requires attribution of the original source through citation of the following two papers:
Kuhlmann, L., Karoly, P., Freestone, D.R., Brinkmann, B.H., Temko, A., Barachant, A., Li, F., Titericz Jr., G., Lang, B.W., Lavery, D., Roman, K., Broadhead, D., Dobson, S., Jones, G., Tang, Q., Ivanenko, I., Panichev, O., Proix, T., Náhlík, M., Grunberg, D.B., Reuben, C., Worrell, G., Litt, B., Liley, D.T.J., Grayden, D.B., & Cook, M.J. (2018) Epilepsyecosystem.org: Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG, Brain, awy210, https://doi.org/10.1093/brain/awy210
Massoud, Y. M., Abdelzaher, M., Kuhlmann, L., & Abd El Ghany, M. A. (2023). General and patient-specific seizure classification using deep neural networks. Analog Integrated Circuits and Signal Processing, 116(3), 205-220.
4. The algorithms of the top performing teams from the ‘Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge’ that can be downloaded under the auspices of Epilepsyecosystem.org are publicly available as open source.
Melbourne Seizure Prediction Trial Seizure Data
This data is licensed under a Creative Commons license with conditions on ATTRIBUTION, NON-COMMERCIAL use and SHARE-ALIKE
NON-COMMERCIAL
Data use is for private individual/research/teaching training and no other purpose
Commercial entities may not use this data for any reason
This data may not be used for patent applications, licensed software, IP or other for-profit use
ATTRIBUTION
Data should be cited as the Melbourne University NeuroVista Seizure Prediction Data https://doi.org/10.26188/5b6a999fa2316 and reference:
Cook, Mark J., et al. "Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study." The Lancet Neurology 12.6 (2013): 563-571.
SHARE-ALIKE
Derivative works must be distributed under a Creative Commons (or equivalent) license.
Derivative works may include (but not limited to) neural networks, classifiers, mathematical models, computational models, networks, figures that are derived substantially from the data
For more information refer to:
Karoly, Philippa J., et al. "Seizure pathways: A model-based investigation." PLoS Computational Biology (2018): https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006403
My Seizure Gauge Data
This data is licensed under a Creative Commons license with conditions on ATTRIBUTION, NON-COMMERCIAL use and SHARE-ALIKE
NON-COMMERCIAL
Data use is for private individual/research/teaching training and no other purpose
Commercial entities may not use this data for any reason
This data may not be used for patent applications, licensed software, IP or other for-profit use
ATTRIBUTION
Data should be cited as the Epilepsy Foundation of America My Seizure Gauge Public Dataset, and should reference the following two manuscripts:
Kuhlmann, L., Karoly, P., Freestone, D.R., et al., (2018) Epilepsyecosystem.org: Crowd-Sourcing Reproducible Seizure Prediction with Long-Term Human Intracranial EEG, Brain, awy210, https://doi.org/10.1093/brain/awy210
Nasseri, M, Nurse, E, Glasstetter, M, et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia. 2020; 00: 1– 11. https://doi.org/10.1111/epi.16527.
SHARE-ALIKE
Derivative works must be distributed under a Creative Commons (or equivalent) license.
Derivative works may include (but not limited to) neural networks, classifiers, mathematical models, computational models, networks, figures that are derived substantially from the data