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http://dx.doi.org/10.14400/JDC.2015.13.10.287

Detection of Epileptic Seizure Based on Peak Using Sequential Increment Method  

Lee, Sang-Hong (Department of Computer Science & Engineering, Anyang University)
Publication Information
Journal of Digital Convergence / v.13, no.10, 2015 , pp. 287-293 More about this Journal
Abstract
This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.
Keywords
Epilepsy; Fuzzy Neural Networks; Wavelet Transforms; Phase Space Reconstruction;
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Times Cited By KSCI : 1  (Citation Analysis)
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