Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network  

作　　者：Nabil SABOR Yongfu LI Zhe ZHANG Yu PU Guoxing WANG Yong LIAN 

机构地区：[1]Department of Micro-Nano Electronics,Shanghai Jiao Tong University,Shanghai 200240,China [2]MoE Key Lab of Artificial Intelligence,Shanghai Jiao Tong University,Shanghai 200240,China [3]Electrical Engineering Department,Assiut University,Assiut 71516,Egypt [4]Alibaba DA MO Academy,Sunnyvale CA 94085,USA

出　　处：《Science China(Information Sciences)》2021年第6期199-217,共19页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key Research and Development Program of China (Grant No. 2019YFB2204500);National Natural Science Foundation of China (Grant No. 61874171);Alibaba Group through Alibaba Innovative Research (AIR) Program。

摘　　要：Detection of interictal epileptic discharges(IED) events in the EEG recordings is a critical indicator for detecting and diagnosing epileptic seizures. We propose a key technology to extract the most important features related to epileptic seizures and identifies the IED events based on the interaction between frequencies of EEG with the help of a two-level recurrent neural network. The proposed classification network is trained and validated using the largest publicly available EEG dataset from Temple University Hospital.Experimental results clarified that the interaction between β and β bands, β and γ bands, γ and γ bands,δ and δ bands, θ and α bands, and θ and β bands have a significant effect on detecting the IED discharges.Moreover, the obtained results showed that the proposed technique detects 95.36% of the IED epileptic events with a false-alarm rate of 4.52% and a precision of 87.33% by using only 25 significant features. Furthermore,the proposed system requires only 164 ms for detecting a 1-s IED event which makes it suitable for real-time applications.

关 键 词：interictal epileptic discharges EPILEPSY discrete wavelet transform wavelet bispectrum long short-term memory recurrent neural network 

分 类 号：R742.1[医药卫生—神经病学与精神病学] TP183[医药卫生—临床医学]