Proceedings of the KOSOMBE Conference (대한의용생체공학회:학술대회논문집)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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AUTOMATIC DETECTION OF EPILEPTIFORM ACTIVITY USING WAVELET AND ARTIFICIAL NEURAL NETWORK

웨이브렛과 신경회로망을 이용한 간질 파형 자동 검출

  • Park, H.S. (Dept. of Electronics, Hanyang University) ;
  • Park, C.H. (Dept. of Biomedical Engineering, Hanyang University) ;
  • Lee, Y.H. (Dept. of Electronics, Hanyang University) ;
  • Lee, D.S. (Dept. of Electronics, Hanyang University) ;
  • Kim, S.I. (Dept. of Biomedical Engineering, Hanyang University)
  • 박현석 (한양대학교 전자공학과) ;
  • 박창헌 (한양대학교 의용생체공학과) ;
  • 이용희 (한양대학교 전자공학과) ;
  • 이두수 (한양대학교 전자공학과) ;
  • 김선일 (한양대학교 의용생체공학과)
  • Published : 1997.05.23
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Abstract

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

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