NDLI: Identification of brain regions of interest for epilepsy surgery planning using support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Dian, J.A. Colic, S. Chinvarun, Y. Carlen, P.L. Bardakjian, B.L.
Copyright Year	2015
Description	Author affiliation: Edward S. Rogers Sr. Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada (Dian, J.A.; Colic, S.; Bardakjian, B.L.) \|\| Comprehensive Epilepsy Program & Neurology Unit, Pramongkutklao Hosp., Bangkok, Thailand (Chinvarun, Y.) \|\| Inst. of Biomater. & Biomed. Eng., Univ. of Toronto, Toronto, ON, Canada (Carlen, P.L.)
Abstract	In patients with intractable epilepsy, surgical resection is a promising treatment; however, post surgical seizure freedom is contingent upon accurate identification of the seizure onset zone (SOZ). Identification of the SOZ in extratemporal epilepsy requires invasive intracranial EEG (iEEG) recordings as well as resource intensive and subjective analysis by epileptologists. Expert inspection yields inconsistent localization of the SOZ which leads to comparatively poor post surgical outcomes for patients. This study employs recordings from 6 patients undergoing resection surgery in order to develop an automated and scalable system for identifying regions of interest (ROIs). Leveraging machine learning techniques and features used for seizure detection, a classification system was trained and tested on patients with Engel class I to class IV outcomes, demonstrating superior performance in the class I patients. Further, classification using features based upon both high frequency and low frequency oscillations was best able to identify channels suited for resection. This study demonstrates a novel approach to ROI identification and provides a path for developing tools to improve outcomes in epilepsy surgery.
Starting Page	6590
Ending Page	6593
File Size	811836
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319903
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surgery Epilepsy Support vector machines Feature extraction Electrodes Training Hafnium oxide
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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