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Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.<\/jats:p>","DOI":"10.1155\/2020\/3083910","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T23:31:12Z","timestamp":1598398272000},"page":"1-13","source":"Crossref","is-referenced-by-count":10,"title":["DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network"],"prefix":"10.1155","volume":"2020","author":[{"given":"Zhe","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Micro-Nano Electronics and MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yun","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Neurology, Shanghai Jiao Tong University Affiliated Children\u2019s 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