The aim of this study is to develop an efficient,reliable and automatic epileptic seizureprediction system using scalpeeg measurements with optimalfeature and minimum *** data in interictal and preictal periods fro...
详细信息
The aim of this study is to develop an efficient,reliable and automatic epileptic seizureprediction system using scalpeeg measurements with optimalfeature and minimum *** data in interictal and preictal periods from the CHB-MIT dataset are used for seizure ***,the original signals are decomposed into several frequency bands using a digital wavelet transform(DWT).Then,features including standard deviation(S),log of amplitude(L),quartile(Q) and coefficient of variation(CV) are ***,different combinations of feature vectors are fed into classifiers(support vector machine(SVM) and extreme learning machine(ELM))to classify the above two states(preictal and interictal states).Performance analysis shows that the optimalfeature is CV,the optimal sub-band is 16-31 Hz and the optimaleeg channel can be chosen as FP1-F7,T7-P7,FP1-F3,P3-O1 or *** comparing the classification results,ELM provides a more robust and higher overall accuracy than SVM,and the best average accuracy of both ELM and SVM can reach as high as 100%.
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