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Seizure Prediction Based on Multidimensional EEG Spatial Matrix and Residual Network Structure

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

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Abstract

Epilepsy is a common chronic neurological brain disorder with a complex etiology. Epilepsy prediction, one of the most challenging data analysis tasks in chronic brain disorders, has attracted much attention from many researchers. This paper proposes a seizure prediction algorithm based on a multidimensional EEG spatial matrix and residual network. Since EEG signals are random, non-stationary, and non-linear, a single feature extraction method cannot represent EEG signal characteristics in many aspects, while entropy can better describe complex EEG signals and identify the dynamic characteristics of EEG signals. Therefore, this paper proposes using sample entropy (SE), permutation entropy (PE), and fuzzy entropy (FE) for feature extraction of EEG signals. Meanwhile, to better describe the irregularity and non-stationary of EEG signals, this paper uses fractal dimension (FD) and fluctuation index (FI) for feature extraction of EEG signals. To better extract the spatial location information of the EEG signal, this paper constructs the spatial location matrix by an electrode distribution map. It reconstructs the one-dimensional feature vector into a two-dimensional feature matrix. The five two-dimensional matrices are superimposed into a three-dimensional matrix as the input form of the network, and the residual network algorithm is used for seizure prediction. The proposed method achieved good performance on the CHB-MIT dataset.

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References

  1. World Health Organization: Neurological disorders: public health challenges. J. Policy Pract. Intellectual Disabilities. 5(1), 1–75(2010)

    Google Scholar 

  2. Viglione, S.S., Walsh, G.O.: Proceedings: epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol. 39(4), 435–436 (1975)

    Google Scholar 

  3. Maiwald, T., Winterhalder, M., Aschenbrenner-Scheibe, R.H., Voss, U., Schulze-Bonhage, A.: Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 194(3–4), 357–368 (2004)

    Article  MATH  Google Scholar 

  4. Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network. IEEE J. Biomed. Health Inform. 24(2), 465–474 (2020)

    Article  Google Scholar 

  5. Truong, N.D., Nguten, A.D., Kuhlmann, L.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018)

    Article  Google Scholar 

  6. Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. 65(9), 2109–2118 (2018)

    Article  Google Scholar 

  7. Yang, X., Zhao, J., Sun, Q., Lu, J., Ma, X.: An effective dual self-attention residual network for seizure prediction. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1604–1613 (2021)

    Article  Google Scholar 

  8. Gao, Y., Chen, X., Liu, A., Liang, D.: Pediatric seizure prediction in scalp EEG using a multi-scale neural network with dilated convolutions. IEEE J. Transl. Eng. Health Med. 10, 1–9 (2022)

    Article  Google Scholar 

  9. Chao, H., Dong, L., Liu, Y., Lu, B.: Emotion recognition from multiband EEG signals using CapsNet. Sensors 19(9), 2212–2228 (2019)

    Article  Google Scholar 

  10. Alazrai, R., Homoud, R., Alwanni, H., Daoud, M.I.: EEG-based emotion recognition using quadratic time-frequency distribution. Sensors 18(8), 2739–2771 (2018)

    Article  Google Scholar 

  11. Li, Y., Zheng, W., Cui, Z., Zong, Y., Ge, S.: EEG emotion recognition based on graph regularized sparse linear regression. Neural Process. Lett. 49(2), 555–571 (2018). https://doi.org/10.1007/s11063-018-9829-1

    Article  Google Scholar 

  12. García-Martínez, B., Fernández-Caballero, A., Zunino, L., Martínez-Rodrigo, A.: Recognition of emotional states from EEG signals with nonlinear regularity and predictability-based entropy metrics. Cogn. Comput. 13(2), 403–417 (2021)

    Article  Google Scholar 

  13. Yu, H., et al.: Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization. Cogn. Neurodyn. 14(1), 35–49 (2019). https://doi.org/10.1007/s11571-019-09551-y

    Article  Google Scholar 

  14. Goldberger, A.L.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  15. Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. Thesis. Massachusetts Institute of Technology (2009)

    Google Scholar 

  16. Mateo, J., Sánchez-Morla, E.M., Santos, J.L.: A new method for removal of powerline interference in ECG and EEG recordings. Comput. Electr. Eng. 45, 235–248 (2015)

    Article  Google Scholar 

  17. Lotte, F.: Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain–computer interfaces. Proc. IEEE 103(6), 871–890 (2015)

    Article  Google Scholar 

  18. Supratak, A., Dong, H., Wu, C., Guo, Y.: DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 1998–2008 (2017)

    Article  Google Scholar 

  19. Tawfik, N.S., Youssef, S.M., Kholief, M.: A hybrid automated detection of epileptic seizures in EEG records. Comput. Electr. Eng. 53, 177–190 (2016)

    Article  Google Scholar 

  20. Esteller, R., Vachtsevanos, G., Echauz, J., Litt, B.: A comparison of waveform fractal dimension algorithms. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 48(2), 177–183 (2001)

    Article  Google Scholar 

  21. Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120(11), 1927–1940 (2009)

    Article  Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  23. Sadeghzadeh, H., Hosseini-Nejad, H., Salehi, S.: Real-time epileptic seizure prediction based on online monitoring of pre-ictal features. Med. Biol. Eng. Compu. 57(11), 2461–2469 (2019). https://doi.org/10.1007/s11517-019-02039-1

    Article  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 61671220), University Innovation Team Project of Jinan (2019GXRC015), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF036).

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250024, China

    Jiahao Zhang, Qingfang Meng & Zewen Wang

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China

    Jiahao Zhang, Qingfang Meng & Zewen Wang

Authors
  1. Jiahao Zhang
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  2. Qingfang Meng
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  3. Zewen Wang
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Corresponding author

Correspondence to Qingfang Meng .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, J., Meng, Q., Wang, Z. (2023). Seizure Prediction Based on Multidimensional EEG Spatial Matrix and Residual Network Structure. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_24

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  • DOI: https://doi.org/10.1007/978-981-99-4749-2_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4748-5

  • Online ISBN: 978-981-99-4749-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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