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EpilepsyNet: Novel automated detection of epilepsy using tr..:
Shu Lih, Oh
;
García López, Salvador
O.S. Lih et al. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Computers in Biology and Medicine 164 (2023) 107312[https://doi.org/10.1016/j.compbiomed.2023.107312]. , 2023
Link:
https://hdl.handle.net/10481/84961
RT Journal T1
EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population
UL https://suche.suub.uni-bremen.de/peid=base-ftunivgranada:oai:digibug.ugr.es:10481_84961&Exemplar=1&LAN=DE A1 Shu Lih, Oh A1 García López, Salvador PB Elsevier YR 2023 K1 Epilepsy K1 Automated diagnosis K1 Transformer deep model K1 Pearson correlation coefficient K1 Positional encoding JF O.S. Lih et al. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Computers in Biology and Medicine 164 (2023) 107312[https://doi.org/10.1016/j.compbiomed.2023.107312] LK http://dx.doi.org/https://hdl.handle.net/10481/84961 DO https://hdl.handle.net/10481/84961 SF ELIB - SuUB Bremen
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