DocumentCode :
1670161
Title :
Automated Detection of Epileptic Seizure Using Artificial Neural Network
Author :
Yuan, Ye ; Li, Yue ; Yu, Dongyan ; Mandic, Danilo P.
Author_Institution :
Coll. of Commun. Eng., Jilin Univ., Changchun
fYear :
2008
Firstpage :
1959
Lastpage :
1962
Abstract :
The embedding dimension of electroencephalogram (EEG) time series is used as the input feature of artificial neural network for detecting epileptic seizure automatedly. Cao´s method is applied for computing the embedding dimension of normal and epileptic EEG time series. The probabilistic neural networks (PNN) is used in this paper for the automated detection of epilepsy. The results show that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper. An interesting phenomenon is also found by Cao´s method that normal EEG time series is of randomness, whereas epileptic EEG time series is of some degree of determinacy, which means that epileptic EEG time series can be predicted well.
Keywords :
electroencephalography; medical signal detection; medical signal processing; neural nets; probability; time series; Cao´s method; EEG; artificial neural network; automated epileptic seizure detection; electroencephalogram time series; embedding dimension computation; probabilistic neural networks; Artificial neural networks; Biological neural networks; Brain modeling; Differential equations; Electroencephalography; Embedded computing; Epilepsy; Nonlinear dynamical systems; Sampling methods; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1747-6
Electronic_ISBN :
978-1-4244-1748-3
Type :
conf
DOI :
10.1109/ICBBE.2008.819
Filename :
4535699
Link To Document :
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