NDLI: Reliable seizure prediction from EEG data

Content Provider	IEEE Xplore Digital Library
Author	Cherkassky, V. Veber, B. Jieun Lee Han-Tai Shiao Patterson, N. Worrell, G.A. Brinkmann, B.H.
Copyright Year	2015
Description	Author affiliation: Dept. of Veterinary Clinical Sci., Univ. of Minnesota, Minneapolis, MN, USA (Patterson, N.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA (Cherkassky, V.; Veber, B.; Jieun Lee; Han-Tai Shiao) \|\| Mayo Syst. Electrophysiology Lab., Mayo Clinic, Rochester, MN, USA (Worrell, G.A.; Brinkmann, B.H.)
Abstract	There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear evidence that many patients have changes in EEG signal prior to seizures, development of robust seizure prediction methods remains elusive [1]. We argue that the main issue for development of effective EEG-based predictive models is an apparent disconnect between clinical considerations and dataanalytic modeling assumptions. We present an SVM-based system for seizure prediction, where design choices and performance metrics are clearly related to clinical objectives and constraints. This system achieves very accurate prediction of preictal and interictal EEG segments in dogs with naturally occurring epilepsy. However, our empirical results suggest that good prediction performance may be possible only if the training data set has sufficiently many preictal segments, i.e. at least 6-7 seizure episodes.
Starting Page	1
Ending Page	8
File Size	1355587
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479919604
DOI	10.1109/IJCNN.2015.7280327
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	Ireland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Electroencephalography Reliability Analytical models Data models Predictive models Support vector machines unbalanced data epilepsy iEEG patient-specific modeling predictive data analytics seizure prediction SVM classification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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