Epileptic Seizure Detection Using Deep Convolutional Network

被引：0
|
作者：
Zou, Lang [1 ,2 ,3 ]
Liu, Xiaofeng [1 ,2 ,3 ]
Jiang, Aimin [1 ,2 ,3 ]
Zhou, Xu [1 ,2 ,3 ]
机构：
[1] Hohai Univ, Coll IoT Engn, Changzhou, Peoples R China
[2] Jiangsu Key Lab Special Robot Technol, Changzhou, Peoples R China
[3] Changzhou Key Lab Special Robot & Intelligent Tec, Changzhou, Peoples R China
来源：
2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP) | 2018年
关键词：
Convolutional networks; Epileptic seizure detection; Scalp EEG;
D O I：
暂无
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：
In this paper, a patient specific seizure detection system using channel-restricted convolutional neural network(CR-CNN) with deep structure is represented. The binary patterns of brainwave activity reflected on ictal and interictal EEG are auto-memorized based on back-propagation mechanism. It is well trained using massive historical scalp EEG data of 23 pediatric patients with epilepsy from CHB-MIT database. Experimental results demonstrate that the proposed detector achieves the state of the art performance. The average false alarms rate reaches 0.12 per hour and only one out of the 167 seizures is missed.
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收藏
页数：4
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