NDLI: Seizure prediction using cost-sensitive support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Netoff, T. Yun Park Parhi, K.
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455, USA (Yun Park) \|\| Faculty of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455, USA (Parhi, K.) \|\| Faculty of Biomedical Engineering, University of Minnesota, 312 Church St SE, Minneapolis, MN 55455, USA (Netoff, T.)
Abstract	Approximately 300,000 Americans suffer from epilepsy but no treatment currently exists. A device that could predict a seizure and notify the patient of the impending event or trigger an antiepileptic device would dramatically increase the quality of life for those patients. A patient-specific classification algorithm is proposed to distinguish between preictal and interictal features extracted from EEG recordings. It demonstrates that the classifier based on a Cost-Sensitive Support Vector Machine (CSVM) can distinguish preictal from interictal with a high degree of sensitivity and specificity, when applied to linear features of power spectrum in 9 different frequency bands. The proposed algorithm was applied to EEG recordings of 9 patients in the Freiburg EEG database, totaling 45 seizures and 219-hour-long interictal, and it produced sensitivity of 77.8% (35 of 45 seizures) and the zero false positive rate using 5-minute-long window of preictal via double-cross validation. This approach is advantageous, for it can help an implantable device for seizure prediction consume less power by real-time analysis based on extraction of linear features and by offline optimization, which may be computationally intensive and by real-time analysis.
Starting Page	3322
Ending Page	3325
File Size	1767615
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333711
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Electroencephalography Epilepsy Support vector machine classification Feature extraction Sensitivity and specificity Diseases Costs Prediction algorithms Machine learning algorithms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
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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