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

By Mark J. Cook, Terence O’Brien, Samuel F. Berkovic, M. Murphy, Andrew Morokoff, G. Fabinyi, W. D'Souza, R. Yerra, J. Archer, L. Litewka and 7 more. Lancet Neurology, 2013

815 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/9a64356480b57aaecd1ea0abb1500d69ede03c9a

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Citations in the corpus

Artificial Intelligence in Healthcare: Review and Prediction Case Studies (2020, neutral): “It is very important to predict seizures so as to minimize their impact on patients [82].”
Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. (2014, methodology): “An Australian group [28] implanted 15patientswith a seizure advisory device and found high rates of sensitivity, ranging from 65 to 100%, with no significant impact on quality of life, severity of seizures, and measures for anxiety and depression disorders.”
On the nature of seizure dynamics. (2014, background): “Like most clinical and experimental EEG, seizures are recorded in alternating current mode, thus filtering any slow variations of the field potential (Fig.”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “This two stage framework is evaluated using the dataset proposed in [98] and the system achieve a mean sensitivity of 69%.”
Focal Onset Seizure Prediction Using Convolutional Networks (2018, background): “described in the appendix of [6], and two of the top three seizure prediction algorithms from Kaggle [5].”