DocumentCode :
1939806
Title :
Focal epileptic seizure forecasting in EEG signals using wavelet transform and artificial neural networks
Author :
Kulasuriya, K. A Helini ; Perera, M.U.S.
Author_Institution :
Dept. of Comput., Inf. Inst. of Technol., Univ. of Westminster, Colombo, Sri Lanka
fYear :
2011
fDate :
25-27 Nov. 2011
Firstpage :
296
Lastpage :
301
Abstract :
Seizure prediction has become a major field of neurological research, because of the suggestion of recent research that electrophysiological changes develop minutes to hours, before the actual clinical onset in focal epileptic seizures. This paper describes a novel approach for forecasting focal epileptic seizures, by applying statistical analysis methods and classifying, the features derived from intracranial Electroencephalographic (EEG) recordings, of brain activity. The decision making consists of two stages; initially the signal features are extracted by applying wavelet transform (WT) and then an artificial neural network (ANN) model, which is a supervised learning-based algorithm classifier, used for signal classification. Wavelet transform is an effective tool for analysis of transient events in nonstationary signals, such as EEGs. The performance of the ANN classifier is evaluated in terms of sensitivity, specificity and classification accuracy. The obtained classification accuracy confirms that the proposed scheme has potential in classifying EEG signals.
Keywords :
electroencephalography; feature extraction; learning (artificial intelligence); medical signal processing; neural nets; signal classification; statistical analysis; wavelet transforms; ANN; EEG signals; artificial neural networks; focal epileptic seizure forecasting; intracranial electroencephalographic recording; neurological research; seizure prediction; signal classification; signal feature extraction; statistical analysis methods; supervised learning based algorithm classifier; wavelet transform; Accuracy; Artificial neural networks; Discrete wavelet transforms; Electroencephalography; Feature extraction; Wavelet analysis; Artificial Neural Networks (ANNs); Discrete Wavelet Transform (DWT); Electroencephalogram (EEG); Epilepsy; Seizure Prediction; Seizure forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control System, Computing and Engineering (ICCSCE), 2011 IEEE International Conference on
Conference_Location :
Penang
Print_ISBN :
978-1-4577-1640-9
Type :
conf
DOI :
10.1109/ICCSCE.2011.6190540
Filename :
6190540
Link To Document :
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