Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2008.4670889
Title: Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier
Authors: Pan, Y.
Ge, S.S. 
Mamun, A.A. 
Tang, F.R.
Keywords: Locally linear embedding
Seizures detection
Weighted distance measurement
Weighted locally linear embedding
Issue Date: 2008
Source: Pan, Y.,Ge, S.S.,Mamun, A.A.,Tang, F.R. (2008). Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier. 2008 IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2008 : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2008.4670889
Abstract: To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG- based, brain-state identification method which could form the basis for detecting epileptic seizure. We aim to classify the EEG signals and diagnose the epileptic seizures directly by using weighted locally linear embedding (WLLE) and support vector machine (SVM). Firstly, we use WLLE to do feature extraction of the EEG signal to obtain more compact representations of the internal characteristic and structure in the original data, which captures the information necessary for further manipulations. Then, SVM classifier is used to identify the seizures onset state from normal state of the patients. © 2008 IEEE.
Source Title: 2008 IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2008
URI: http://scholarbank.nus.edu.sg/handle/10635/69891
ISBN: 9781424416745
DOI: 10.1109/ICCIS.2008.4670889
Appears in Collections:Staff Publications

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