Title :
Singular values as a detector of epileptic seizures in EEG signals
Author :
Shahid, A. ; Kamel, N. ; Malik, A.S.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. Teknol. PETRONAS, Tronoh, Malaysia
Abstract :
This paper introduces a new method based on the Singular Values of EEG signals for the detection of epileptic seizures. Singular Value Decomposition was performed on an EEG signal in epochs of 8 seconds and Singular Values were extracted from each epoch. These singular values were fed into Support Vector Machine (SVM) for a binary classification between epileptic seizure and non- seizure events. Singular Values of EEG signals proved to be a very good feature for the detection of epileptic seizures and gave a classification accuracy of 90%, and an average sensitivity and specificity of 91% and 89%, respectively.
Keywords :
electroencephalography; feature extraction; medical disorders; medical signal detection; medical signal processing; neurophysiology; signal classification; singular value decomposition; support vector machines; EEG signal epochs; EEG signal feature; EEG signal singular values; SVM; average sensitivity; average specificity; binary classification; classification accuracy; epileptic seizure detection; epileptic seizure event classification; nonseizure event classification; singular value decomposition; singular value extraction; support vector machine; time 8 s; Accuracy; Classification algorithms; Electroencephalography; Feature extraction; Pediatrics; Sensitivity; Support vector machines; Electroencephalography (EEG); Epileptic Seizure Detection; Singular Value Decomposition (SVD); Support Vector Machine (SVM);
Conference_Titel :
Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4799-4654-9
DOI :
10.1109/ICIAS.2014.6869459
