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- W4294250612 abstract "Epilepsy may occur with a genetic disorder or an acquired brain injury, such as a trauma or stroke. It is a type of disorder in which activity of nerve cell in the brain is disturbed, causing seizures. Electroencephalogram (EEG) is used to analyze the Epileptic seizure which is a very serious nervous system disorder. In this work detection of epilepsy disease is approached by a Graph Signal Processing (GSP) technique (with computing the Graph Discrete Fourier Transform (GDFT)). GDFT coefficients are produced on the Eigen space of Laplacian matrix with the help of EEG data points. The Laplacian matrix is calculated from the weighted graph designed for EEG signal. The proposed GDFT based feature vectors are used to detect the epilepsy seizure class from the given EEG signal and classify by using Stationarity ratio and TIK-norm. By observing the simulated results one can analyze that the proposed GDFT based total features can discover epileptic seizure with 97% accuracy which is obtained from Gaussian Weighted Graph. To provide a nice compact format to encode the structure within the data, new tools are being developed in GSP." @default.
- W4294250612 created "2022-09-02" @default.
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- W4294250612 date "2022-01-01" @default.
- W4294250612 modified "2023-10-16" @default.
- W4294250612 title "Detection of Epilepsy Using Graph Signal Processing of EEG Signals with Three Features" @default.
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- W4294250612 doi "https://doi.org/10.1007/978-981-19-1520-8_46" @default.
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