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- W3119544075 abstract "Epilepsy is a chronic neurological disorder. The cause are unprovoked recurrent seizures that patients experience. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG). The EEG measures the electrical activity of the brain. The patients have to be monitored as to detect an epileptic episode early on in order to prevent associate risks. Research in the past has used a combination of time and frequency features for the automatic recognition of epileptic seizures. An classification approach has been used to automatically detect epileptic seizures. In this paper, the epileptic seizure recognition data set is used for the investigation. Two fusion methods, ensemble and Choquet fuzzy integral, are compared using different deep neural network architectures. To aid the comparison, evaluation measures such as confusion matrix, AUC and accuracy are used in conjunction with MSE and RMSE. The results of the experiments show that the Choquet fuzzy integral fusion method outperforms the ensemble method. In addition, other state-of-the-art classification methods are also outperformed by the Choquet fuzzy integral fusion method." @default.
- W3119544075 created "2021-01-18" @default.
- W3119544075 creator A5015130765 @default.
- W3119544075 date "2020-12-01" @default.
- W3119544075 modified "2023-09-24" @default.
- W3119544075 title "Multi-label Classification for Epileptic Seizure Recognition: Deep Neural Network Ensemble versus Choquet Fuzzy Integral Fusion" @default.
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- W3119544075 doi "https://doi.org/10.1109/ssci47803.2020.9308541" @default.
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