디지털융복합연구 (Journal of Digital Convergence)

  • 제13권10호
  • /
  • Pages.287-293
  • /
  • 2015
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

점증적 증가를 이용한 첨점 기반의 간질 검출

Detection of Epileptic Seizure Based on Peak Using Sequential Increment Method

  • 이상홍 (안양대학교 컴퓨터공학과)
  • Lee, Sang-Hong (Department of Computer Science & Engineering, Anyang University)
  • 투고 : 2015.08.20
  • 심사 : 2015.10.20
  • 발행 : 2015.10.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 신호 처리 기술과 가중 퍼지소속함수 기반 신경망 (Neural Network with Weighted Fuzzy Membership Functions; NEWFM)을 이용하여 간질을 검출하는 방안을 제안하였다. 신호 처리 기술로는 웨이블릿 변환(Wavelet Transform), 점증적 증가 방법, 위상공간 재구성(Phase Space Reconstruction)을 이용하였다. 신호 처리 기술의 첫 번째 단계에서는 웨이블릿 변환을 이용하여 뇌파로부터 웨이블릿 계수를 추출하였다. 두 번째 단계에서는 점증적 증가 방법을 이용하여 웨이블릿 계수로부터 첨점(Peak)을 추출하였다. 세 번째 단계에서는 위상공간 재구성을 이용하여 추출된 첨점으로부터 3차원 다이어그램을 생성하였다. NEWFM의 입력으로 사용할 16개의 특징을 추출하기 위하여 유클리드 거리와 통계적 방법을 이용하였다. 이들 16개의 특징을 NEWFM의 입력으로 사용하여 97.5%, 100%, 95%의 정확도, 특이도, 민감도를 각각 구하였다.

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

키워드

참고문헌

  1. Admi, H. and Shaham, B., Living with epilepsy: ordinary people coping with extraordinary situations, Qualitative Health Research, Vol.17, pp.1178-1187, 2007. https://doi.org/10.1177/1049732307307548
  2. Korean Neurological Association. Neurology, Seoul: Koonja Publishing Co., 2007.
  3. R. Sharma, R. B. Pachori, Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions, Expert Systems with Applications, Vol.42, pp.1106-1117, 2015. https://doi.org/10.1016/j.eswa.2014.08.030
  4. S. -H. Lee, J. S. Lim, J. -K. Kim, J. Yang, Y. Lee, Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance, Computer Methods and Programs in Biomedicine, Vol.116, pp.10-25, 2014. https://doi.org/10.1016/j.cmpb.2014.04.012
  5. Kemal Polat and Salih Gunes, Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals, Expert Systems with Applications, Vol.34, Issue 3, pp.2039-2048, 2008 https://doi.org/10.1016/j.eswa.2007.02.009
  6. Avci E, Hanbay D, Varol A. An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition, Expert Syst Appl, Vol.33, pp.582-589, 2007. https://doi.org/10.1016/j.eswa.2006.06.001
  7. Guler I, Ubeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, J Neurosci Methods, Vol.148, pp.113-121, 2005. https://doi.org/10.1016/j.jneumeth.2005.04.013
  8. M. E. Menshawy, A. Benharref, M. Serhani, An automatic mobile-health based approach for EEG epileptic seizures detection, Expert Systems with Applications, Vol.42, 7157-7174, 2015 https://doi.org/10.1016/j.eswa.2015.04.068
  9. F Shayegha, S Sadria, R Amirfattahia, K Ansari-Aslb. A model-based method for computation ofcorrelation dimension, Lyapunov exponents andsynchronization from depth-EEG signals, COMPUT METH PROG BIO, Vol.113, pp.323-337, 2014. https://doi.org/10.1016/j.cmpb.2013.08.014
  10. Guler NH, Ubeyli̇ ED, Guler I. Recurrent neural networks employing Lyapunov exponents for EEG signal classification, Expert Sys Appl, Vol.25, pp.506-514, 2005.
  11. Abdulhamit Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, Vol.32, Issue 4, pp.1084-1093, 2007. https://doi.org/10.1016/j.eswa.2006.02.005
  12. Guler I, Ubeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, J Neurosci Methods, Vol.148, pp.113-121, 2005. https://doi.org/10.1016/j.jneumeth.2005.04.013
  13. Y. Songa, J. Crowcroft, J. Zhang, Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine, Journal of Neuroscience Methods, Vol.210, pp.132-146, 2012. https://doi.org/10.1016/j.jneumeth.2012.07.003
  14. S. -H. Lee and J. S. Lim, Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM, Journal of Internet Computing and Services, Vol.10, No.5, pp.127-133, 2009.
  15. J. S. Lim, Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System, IEEE Transactions on Neural Networks, Vol.20, No.3, pp.522-527, 2009. https://doi.org/10.1109/TNN.2008.2012031