Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform

웨이블릿 변환과 힐버트 변환을 이용한 간질 파형 분류

  • Lee, Sang-Hong (Department of Computer Science & Engineering, Anyang University)
  • 이상홍 (안양대학교 컴퓨터공학과)
  • Received : 2016.02.29
  • Accepted : 2016.04.20
  • Published : 2016.04.28
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Abstract

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

본 논문에서는 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions; NEWFM) 기반의 웨이블릿 변환(wavelet transform)과 힐버트 변환(Hilbert transform)에 의해 추출한 첨점(peak)을 사용하여 뇌파(EEG)로부터 정상 파형과 간질 파형을 분류하는 새로운 방안을 제안하였다. NEWFM의 입력을 추출하는데 다음과 같은 3개의 단계가 수행되었다. 첫 번째 단계에서는 뇌파로부터 잡음을 제거하기 위해서 웨이블릿 변환을 사용하였다. 두 번째 단계에서는 웨이블릿 계수로부터 첨점(peak)을 추출하기 위해서 힐버트 변환을 사용하였다. 또한 크기가 큰 첨점을 추출하기 위해서 첨점의 평균값보다 큰 첨점만을 선택하였다. 세 번째 단계에서는 통계적 방법을 이용하여 첨점으로부터 NEWFM의 입력으로 사용할 16개의 특징을 추출하였다. NEWFM은 이들 16개의 특징을 입력으로 사용하여 99.25%, 99.4%, 99%의 정확도, 특이도, 민감도를 각각 구하였다. 향후 연구에서는 특징선택을 이용하여 16개의 특징으로부터 좋은 특징을 선택하여 정확도를 향상시킬 계획이다.

Keywords

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