【摘 要】
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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 id
【机 构】
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Department of Micro-Nano Electronics,Shanghai Jiao Tong University,Shanghai 200240,China;MoE Key Lab
论文部分内容阅读
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
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