Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Detection of Epileptic Seizure Based on Peak Using Sequential Increment Method

점증적 증가를 이용한 첨점 기반의 간질 검출

  • Lee, Sang-Hong (Department of Computer Science & Engineering, Anyang University)
  • 이상홍 (안양대학교 컴퓨터공학과)
  • Received : 2015.08.20
  • Accepted : 2015.10.20
  • Published : 2015.10.28
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Abstract

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.

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.

Keywords

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