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- W4281567360 endingPage "103820" @default.
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- W4281567360 abstract "Epilepsy is one of the most common complex brain disorders which is a chronic non-communicable disease caused by paroxysmal abnormal super-synchronous electrical activity of brain neurons. This paper proposed an electroencephalogram (EEG) based real-time approach to detect epilepsy seizures. Discrete wavelet transform and eight eigenvalues’ algorithms are applied to extract features in different sub-frequency bands. Then support vector machine is employed for three-classes classification of health control, seizure free and seizure active, and finally RUSBoosted tree Ensemble method is used for real-time seizure onset detection. The proposed algorithm is evaluated using two public datasets: one short-term dataset named UB and one long-term dataset named CHB-MIT. The results show that the algorithm achieves 97% accuracy and 96.67% sensitivity in the three-classes classification of health control, seizure-free and seizure-active groups in UB dataset, and 96.38% accuracy, 96.15% sensitivity, 3.24% false positive rate for the real time seizure onset detection in CHB-MIT Dataset." @default.
- W4281567360 created "2022-05-27" @default.
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- W4281567360 date "2022-08-01" @default.
- W4281567360 modified "2023-10-07" @default.
- W4281567360 title "An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods" @default.
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- W4281567360 doi "https://doi.org/10.1016/j.bspc.2022.103820" @default.
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