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The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN based on Swin transformer (ST) and 2D convolutional neural network (2DCNN) is proposed. Time-frequency features extracted by short-term Fourier transform (STFT) are taken as the input of STCNN. ST blocks are used in STCNN to capture the global information and long-term dependencies of EEGs. Meanwhile, the 2DCNN blocks are adopted to capture the local information and short-term dependent features. The combination of the two blocks can fully exploit the seizure-related information thus improve the prediction performance. Comprehensive experiments are performed on the CHB-MIT scalp EEG dataset. The average seizure prediction sensitivity, the area under the ROC curve (AUC) and the false positive rate (FPR) are 92.94%, 95.56% and 0.073, respectively. <\/jats:p>","DOI":"10.1142\/s0129065723500569","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T07:59:15Z","timestamp":1693209555000},"source":"Crossref","is-referenced-by-count":25,"title":["Hybrid Network for Patient-Specific Seizure Prediction from EEG Data"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5524-2053","authenticated-orcid":false,"given":"Yongfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. 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