Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network

Yang Li1, Yü Liu2, Weigang Cui2, Yuzhu Guo2, Hui Huang3, Zhongyi Hu3
1Department of Automation Sciences and Electrical Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China
2Department of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China
3Intelligent Information Systems Institute, Wenzhou University, Wenzhou, China

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Tài liệu tham khảo

10.1109/MSPCT.2011.6150470

saputro, 2019, Seizure type classification on EEG signal using support vector machine, J Phys Conf Ser, 1201

10.3390/brainsci9050115

10.1155/2007/80510

10.1109/TNSRE.2015.2505238

10.1109/ISPCC.2012.6224361

10.1016/j.compbiomed.2017.09.017

10.1109/TNSRE.2019.2913400

10.1109/TNSRE.2015.2441835

10.3390/e19060222

10.1109/TNNLS.2018.2886414

10.1109/CVPR.2018.00745

10.1109/TNSRE.2019.2915621

zhao, 2017, InfoVAE: Information maximizing variational autoencoders, arXiv 1706 02262

10.1109/CVPR.2016.90

strang, 1996, Wavelets and Filter Banks

chang, 2012, Channel selection for epilepsy seizure prediction method based on machine learning, Proc Annu Int Conf IEEE Eng Med Biol Soc, 5162

10.1016/j.knosys.2018.10.029

roy, 2019, Machine learning for seizure type classification: Setting the benchmark, arXiv 1902 01012

10.1109/TNSRE.2018.2850308

10.1002/hbm.23730

10.1142/S012906571850003X

10.1109/TBME.2003.810706

thodoroff, 2016, Learning robust features using deep learning for automatic seizure detection, Proc Mach Learn Healthcare Conf, 178

10.1109/TGRS.2018.2878510

10.1109/TNSRE.2016.2611601

10.1109/JBHI.2018.2871678

10.1109/TBME.2017.2650259

10.1109/JBHI.2017.2654479

kingma, 2013, Auto-encoding variational Bayes, arXiv 1312 6114

10.1109/CVPR.2019.00060

10.3389/fninf.2018.00083

10.1103/PhysRevE.64.061907

10.1016/j.jbi.2014.02.005

shoeb, 2009, Application of machine learning to epileptic seizure onset detection and treatment