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

[11] Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network
Das, Shupta
Mumu, Suraiya Akter
Akhand, M. A. H.
Salam, Abdus
Kamal, Md Abdus Samad
INFORMATION, 2024, 15 (05)
[12] A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network
Shen, Mingkan
Yang, Fuwen
Wen, Peng
Song, Bo
Li, Yan
HELIYON, 2024, 10 (11)
[13] A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images
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Nagaraja Rao Suryakari
Ramana Reddy Patil
Multimedia Tools and Applications, 2023, 82 : 3185 - 3227
[14] DEVELOPMENT OF AN EFFICIENT DRIVER DROWSINESS DETECTION SYSTEM USING EEG AND HEART RATE SENSOR DATA FOR MEASURING ACCURACY AND PRECISION USING NEURAL NETWORK ALGORITHM IN COMPARISON WITH COMPUTER VISION FACIAL FEATURE TECHNIQUE
Jeevan, C.
Ganapathy, Kirupa
INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION, 2022, 14 (03) : 5415 - 5424
[15] a diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images (June, 10.1007/s11042-022-13381-2, 2022)
Rela, Munipraveena
Suryakari, Nagaraja Rao
Patil, Ramana Reddy
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (03) : 3229 - 3229

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