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- W2895567635 abstract "Epilepsy is a common neurological disorder that can affect people of all ages, characterized by recurrent seizures. Most of the epileptic patients have more than one seizure. Seizure prediction helps to reduce patient's anxiety and thus improve the quality of life. The main objective of this work is to predict epileptic seizures at the earliest by analyzing Electroencephalogram (EEG) signals with minimum False Detection Rate (FDR). Energy and Inter Quartile range (IQR) of raw EEG signal per window per channel is calculated and this forms the feature vector. In order to reduce feature vector dimension different Pythagorean means across the channels are evaluated. This algorithm is tested on publicly available long-term EEG database from Physionet ATM for different classifiers LDA, QDA and kNN. The performance of different classifiers is evaluated and compared in this work. The average prediction time obtained was 31.6 minutes at an FDR of 1.3. The results are found to be promising." @default.
- W2895567635 created "2018-10-12" @default.
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- W2895567635 date "2018-04-01" @default.
- W2895567635 modified "2023-09-27" @default.
- W2895567635 title "Automatic Epileptic Seizure Prediction in Scalp EEG" @default.
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- W2895567635 doi "https://doi.org/10.1109/icics.2018.00063" @default.
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