NDLI: Neural network analysis of seizure activity in subdural EEG

Content Provider	IEEE Xplore Digital Library
Author	Bates, R.R. Mingui Sun Scheuer, M.L. Sclabassi, R.J.
Copyright Year	2003
Description	Author affiliation: Dept. of Neurological Surg., Pittsburgh Univ., PA, USA (Bates, R.R.; Mingui Sun)
Abstract	Detection of the onset of seizure-activity in the EEG record of an epileptic patient is an important step in the localization of seizure foci. This research examines the use of three recurrent neural-network architectures to detect seizure-activity onset in multi-channel subdural EEG data. The neural networks' output represents the degree of seizure-like activity for each of the 94 channels and four closest neighboring electrodes. A decrease in the standard-deviation of the neural network output at each seizure-like event was noted. Also, by spectrogram analysis, a large increase in the low-frequency components of the variation at the time of the seizure event in each sample was seen. This suggests that, at the onset of seizure-like activity: 1) the magnitude of the signal standard-deviation across the subdural space is small; 2) the time-dependent change of the standard-deviation is small; 3) the signal power is concentrated in a small range of low-frequency components. These results suggest the entrainment of epileptogenic neural components.
Sponsorship	IEEE Circuits and Syst. Soc. (CASS) IEEE R10 CASS IEEE Beijing Section Chinese Neural Networks Council Circuits and Information Processing Committee, CASS-CIE Southeast Univ., China
Starting Page	11
Ending Page	14
File Size	317593
Page Count	4
File Format	PDF
ISBN	0780377028
DOI	10.1109/ICNNSP.2003.1279201
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Recurrent neural networks Data analysis Neural networks Epilepsy Artificial neural networks Nervous system Electroencephalography Signal analysis Biological neural networks
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
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