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Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform
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  • Journal title : Journal of Digital Convergence
  • Volume 14, Issue 4,  2016, pp.277-283
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2016.14.4.277
 Title & Authors
Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform
Lee, Sang-Hong;
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This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.
Epilepsy;Fuzzy Neural Networks;Wavelet Transforms;Hilbert Transform;EEG;
 Cited by
Ministry of Health & Welfare,

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