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

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Abstract

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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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2019YFB2204500), National Natural Science Foundation of China (Grant No. 61874171), and Alibaba Group through Alibaba Innovative Research (AIR) Program.

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Authors and Affiliations

  1. Department of Micro-Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Nabil Sabor, Yongfu Li, Zhe Zhang, Guoxing Wang & Yong Lian

  2. MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200240, China

    Nabil Sabor, Yongfu Li, Zhe Zhang, Guoxing Wang & Yong Lian

  3. Electrical Engineering Department, Assiut University, Assiut, 71516, Egypt

    Nabil Sabor

  4. Alibaba DAMO Academy, Sunnyvale, CA, 94085, USA

    Yu Pu

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  1. Nabil Sabor
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  2. Yongfu Li
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  3. Zhe Zhang
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  4. Yu Pu
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  5. Guoxing Wang
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  6. Yong Lian
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Correspondence to Nabil Sabor or Guoxing Wang.

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Sabor, N., Li, Y., Zhang, Z. et al. Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network. Sci. China Inf. Sci. 64, 162403 (2021). https://doi.org/10.1007/s11432-020-3100-8

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  • DOI: https://doi.org/10.1007/s11432-020-3100-8

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