DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

被引：12

作者：

Zhang, Zhe ^{[1
,2
]}

Ren, Yun ^{[3
]}

Sabor, Nabil ^{[1
,2
,4
]}

Pan, Jing ^{[3
]}

Luo, Xiaona ^{[3
]}

Li, Yongfu ^{[1
,2
]}

Chen, Yucai ^{[3
]}

Wang, Guoxing ^{[1
,2
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Micronano Elect, Shanghai 200240, Peoples R China

[2] Shanghai Jiao Tong Univ, MoE Key Lab Artificial Intelligence, Shanghai 200240, Peoples R China

[3] Shanghai Jiao Tong Univ, Affiliated Childrens Hosp Shanghai, Dept Neurol, Shanghai 200062, Peoples R China

[4] Assiut Univ, Fac Engn, Elect & Elect Engn Dept, Assiut 71516, Egypt

来源：

JOURNAL OF SENSORS | 2020年 / 2020卷

基金：

美国国家科学基金会;

关键词：

CLASSIFICATION; EPILEPSY;

D O I：

10.1155/2020/3083910

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. 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.

引用

页数：13

共 15 条

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2016 INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC), 2016, : 211 - 212
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MICROPROCESSORS AND MICROSYSTEMS, 2020, 76
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