DocumentCode :
3351591
Title :
Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier
Author :
Pan, Yaozhang ; Ge, Shuzhi Sam ; Mamun, Abdullah Al ; Tang, Feng Ru
Author_Institution :
Social Robot. Lab., Nat. Univ. of Singapore, Singapore
fYear :
2008
fDate :
21-24 Sept. 2008
Firstpage :
358
Lastpage :
363
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.
Keywords :
electroencephalography; feature extraction; medical diagnostic computing; medical signal detection; medical signal processing; neurophysiology; patient diagnosis; signal classification; signal representation; support vector machines; EEG signal seizure detection; EEG-based brain-state identification method; SVM classifier; brain structural disorder diagnosis; electroencephalography; epileptic seizure detection; feature extraction; neurology clinic; signal representation; support vector machine; weighted locally linear embedding; Animals; Clustering algorithms; Electroencephalography; Epilepsy; Feature extraction; Frequency synchronization; Mice; Support vector machine classification; Support vector machines; Transmitters; locally linear embedding; seizures detection; weighted distance measurement; weighted locally linear embedding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
Type :
conf
DOI :
10.1109/ICCIS.2008.4670889
Filename :
4670889
Link To Document :
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