NDLI: Automatic seizure onset detection in drug-resistant epilepsy: A Bayesian optimal solution

Content Provider	IEEE Xplore Digital Library
Author	Santaniello, S. Burns, S.P. Sarma, S.V.
Copyright Year	2012
Description	Author affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA (Santaniello, S.; Burns, S.P.; Sarma, S.V.)
Abstract	Automatic seizure onset detection (ASOD) from intracranial EEG recordings (iEEG) in drug-resistant epilepsy has recently gained large interest. Effective ASOD can shorten the time for offline review of the iEEG recordings, help with the development of unsupervised online monitoring systems, and contribute to responsive neurostimulation. Depending on the application, the performance goals may vary and a tradeoff between detection delay and probability of false positives must be solved. We recently developed a cost-based framework for ASOD. Applied to iEEG recordings, this framework (i) constructs a statistic to distinguish seizure and non-seizure states; (ii) models the dynamics of this statistic in each state and the state transitions; and (iii) develops a Bayesian “quickest detection” strategy to estimate online the transition into seizure from sequential iEEG measurements. A cost function of the detection delay and probability of false positives is explicitly minimized, and the solution is a threshold which decreases over time non-monotonically to avoid responding to rare events that would normally trigger false positives. Applied to iEEG data from two drug-resistant epileptic subjects (202h, 11 seizures), our framework achieved low false positive rates (0.07 false positives/h) and decreased the average detection delay by 30% when compared with existing detection policies.
Starting Page	3189
Ending Page	3194
File Size	443469
Page Count	6
File Format	PDF
ISBN	9781467320658
ISSN	07431546
e-ISBN	9781467320665
e-ISBN	9781467320641
DOI	10.1109/CDC.2012.6426566
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Epilepsy Hidden Markov models Delay Computational modeling Bayesian methods Brain modeling
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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