Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast-Reference-Cited by-同舟云学术

Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast

Published:2020-12-30 Issue:2 Volume:62 Page:371-382
ISSN:0013-9580
Container-title:Epilepsia
language:en
Short-container-title:Epilepsia

Author:

Payne Daniel E.¹²^ORCID,Dell Katrina L.²,Karoly Phillipa J.¹³^ORCID,Kremen Vaclav⁴⁵^ORCID,Gerla Vaclav⁵^ORCID,Kuhlmann Levin²⁶^ORCID,Worrell Gregory A.⁴^ORCID,Cook Mark J.²³^ORCID,Grayden David B.¹²^ORCID,Freestone Dean R.²^ORCID

Affiliation:

1. Department of Biomedical Engineering The University of Melbourne Melbourne Victoria Australia

2. Department of Medicine St Vincent’s Hospital The University of Melbourne Melbourne Victoria Australia

3. Graeme Clark Institute The University of Melbourne Melbourne Victoria Australia

4. Department of Neurology Mayo Clinic Rochester MN USA

5. Czech Institute of Informatics, Robotics, and Cybernetics Czech Technical University in Prague Prague Czech Republic

6. Department of Data Science and AI Faculty of IT Monash University Clayton Victoria Australia

Funder

National Institutes of Health

National Health and Medical Research Council

University of Melbourne

Publisher

Wiley

Subject

Neurology (clinical),Neurology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.16785

Reference51 articles.

1. Epilepsy Foundation.Ei2 community survey.2016.https://www.epilepsy.com/sites/core/files/atoms/files/community‐survey‐report‐2016%20V2.pdf. Accessed 14 Dec 2018.

2. Seizure forecasting from idea to reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute workshop;Dumanis SB;eNeuro,2017

3. The impact of epilepsy from the patient’s perspective I. Descriptions and subjective perceptions

4. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study

5. Epilepsyecosystem.org: crowd‐sourcing reproducible seizure prediction with long‐term human intracranial EEG;Kuhlmann L;Brain,2018

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