NDLI: Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG

Content Provider	IEEE Xplore Digital Library
Author	Mirowski, P.W. Yann LeCun Madhavan, D. Kuzniecky, R.
Copyright Year	2008
Description	Author affiliation: Sch. of Med., NYU Epilepsy Center, New York Univ., New York, NY (Kuzniecky, R.) \|\| Dept. of Neurological Sci., Univ. of Nebraska Med. Center, Omaha, NE (Madhavan, D.) \|\| Courant Inst. of Math. Sci., New York Univ., New York, NY (Mirowski, P.W.; Yann LeCun)
Abstract	Recent research suggests that electrophysiological changes develop minutes to hours before the actual clinical onset in focal epileptic seizures. Seizure prediction is a major field of neurological research, enabled by statistical analysis methods applied to features derived from intracranial Electroencephalographic (EEG) recordings of brain activity. However, no reliable seizure prediction method is ready for clinical applications. In this study, we use modern machine learning techniques to predict seizures from a number of features proposed in the literature. We concentrate on aggregated features that encode the relationship between pairs of EEG channels, such as cross-correlation, nonlinear interdependence, difference of Lyapunov exponents and wavelet analysis-based synchrony such as phase locking. We compare L1-regularized logistic regression, convolutional networks, and support vector machines. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. For each patient, at least one method predicts 100% of the seizures on average 60 minutes before the onset, with no false alarm. Possible future applications include implantable devices capable of warning the patient of an upcoming seizure as well as implanted drug-delivery devices.
Starting Page	244
Ending Page	249
File Size	803962
Page Count	6
File Format	PDF
ISBN	9781424423750
ISSN	15512541
DOI	10.1109/MLSP.2008.4685487
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-16
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Epilepsy Electroencephalography Floors Convolution Brain Electrodes Statistical analysis Prediction methods Machine learning
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
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

Detection of epileptic seizure based on EEG signals

Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG

Efficient EEG analysis for seizure monitoring in epileptic patients

Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal

Automated seizure detection from multichannel EEG signals using Support Vector Machine and Artificial Neural Networks

Seizure detection in EEG using time frequency analysis and SVM

Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

On the benefits of classical multidimensional scaling in Epileptic seizure prediction studies

Seizure detection in EEG signals using support vector machines

Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG

Similar Documents

Detection of epileptic seizure based on EEG signals

Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG

Efficient EEG analysis for seizure monitoring in epileptic patients

Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal

Automated seizure detection from multichannel EEG signals using Support Vector Machine and Artificial Neural Networks

Seizure detection in EEG using time frequency analysis and SVM

Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

On the benefits of classical multidimensional scaling in Epileptic seizure prediction studies

Seizure detection in EEG signals using support vector machines

Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG