반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

CNN을 이용한 뇌전증 발작예측에 관한 연구

A Study on the Epileptic Seizure Prediction using CNN

  • 류상욱 (한양대학교 공과대학 컴퓨터소프트웨어학부) ;
  • 이남화 (한양대학교 공과대학 컴퓨터소프트웨어학부) ;
  • 이연수 (한양대학교 공과대학 컴퓨터소프트웨어학부) ;
  • 조인휘 (한양대학교 공과대학 컴퓨터소프트웨어학부) ;
  • 민경육 (한양대학교 공과대학 융합전자공학부) ;
  • 김택수 ((주)이디에이엘리텍)
  • Ryu, Sanguk (Department of Computer Software, Hanyang University) ;
  • Lee, Namhwa (Department of Computer Software, Hanyang University) ;
  • Lee, Yeonsu (Department of Computer Software, Hanyang University) ;
  • Joe, Inwhee (Department of Computer Software, Hanyang University) ;
  • Min, Kyeongyuk (Department of Electronic Engineering, Hanyang University) ;
  • Kim, Taeksoo (EDA Elitech Co. Ltd.)
  • 투고 : 2020.06.24
  • 심사 : 2020.06.25
  • 발행 : 2020.06.30
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초록

In this paper, the new architecture of seizure prediction using CNN and LSTM and DWT was presented. In the proposed architecture, EEG data was labeled into a preictal and interictal section, and DWT was adopted to the preprocessing process to apply the characteristics of the time and frequency domain of the processed EEG signal. Also, CNN was applied to extract the spatial characteristics of each electrode used for EEG measurement, and LSTM neural network was applied to verify the logical order of the preictal section. The learning of the proposed architecture utilizes the CHB-MIT Scalp EEG dataset, and the sliding window technique is applied to balance the dataset between the number of interictal sections and the number of preictal sections. As a result of the simulation of the proposed architecture, a sensitivity of 81.22% and an FPR of 0.174 were obtained.

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참고문헌

  1. Kandel E, Schwartz J, Jessell T. Principles of Neural Science. New York: McGraw-Hill, Health Professions Division, 2000
  2. Lee, Y., & Lee, Y. H. (2018). Implementation of Moving Object Recognition based on Deep Learning. Journal of the Semiconductor & Display Technology, 17(2), 67-70.
  3. Reuber, M., Fernández, G., Bauer, J., Singh, D. D., & Elger, C. E. (2002). Interictal EEG abnormalities in patients with psychogenic nonepileptic seizures. Epilepsia, 43(9), 1013-1020. https://doi.org/10.1046/j.1528-1157.2002.52301.x
  4. Li, S., Zhou, W., Yuan, Q., Geng, S., & Cai, D. (2013). Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology and Medicine, 43(7), 807-816. https://doi.org/10.1016/j.compbiomed.2013.04.002
  5. Lee, Y. H., & Kim, Y. (2020). Comparison of CNN and YOLO for Object Detection. Journal of the Semiconductor & Display Technology, 19(1), 85-92.
  6. A. H. Shoeb, "Application of machine learning to epileptic seizure onset detection and treatment," Ph.D. dissertation, Massachusetts Inst. Technol., Cambridge, MA, USA, 2009. [Online]. Available: http://hdl.handle.net/1721.1/54669
  7. Cho, E. D., & Kim, G. B. (2018). A Study on Illumination Mechanism of Steel Plate Inspection Using Wavelet Synthetic Images. Journal of the Semiconductor & Display Technology, 17(2), 26-31.
  8. Truong, N. D., Nguyen, A. D., Kuhlmann, L., Bonyadi, M. R., Yang, J., Ippolito, S., & Kavehei, O. (2018). Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks, 105, 104-111. https://doi.org/10.1016/j.neunet.2018.04.018