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• W2895873090 abstract "Abstract The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%." @default.
• W2895873090 created "2018-10-26" @default.
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• W2895873090 date "2018-10-19" @default.
• W2895873090 modified "2023-10-12" @default.
• W2895873090 title "Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction" @default.
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• W2895873090 doi "https://doi.org/10.1038/s41598-018-33969-9" @default.
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