Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Implementation of EEG Artifact Removal Process Based on Bispectrum Analysis

바이스펙트럼 분석 기반의 뇌파 Artifact 제거 프로세스 구현

  • Park, Junmo (School of Electronic and Biomedical Engineering, Tongmyong University)
  • 박준모 (동명대학교 전자및의용공학부)
  • Received : 2019.06.14
  • Accepted : 2019.06.29
  • Published : 2019.06.30
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Abstract

In this study, bispectrum analysis method introduced to reduce variability of SEF(spectral edge frequency) and MF(median frequency), which are the anesthetic depth indexes extracted by EEG spectral analysis. Bispectrum analysis is an analytical method that can confirm the nonlinearity of EEG. Signal measurement and analysis in the surgical environment should take into consideration various external artifact factors. Bispectrum analysis can confirm the presence of externally introduced artifacts, thereby effectively eliminating artifacts that affect the EEG signal. By applying bispectrum parameters, real-time variability of the anesthetic depth parameters SEF, MF could be reduced. Elimination of variability makes it possible to use SEF, MF as a real-time index during surgery.

본 연구에서는 뇌파의 스펙트럼 분석에 의해 추출되는 마취심도 지표인 SEF(spectral edge freqency), MF(median frequency)의 가변성 감소를 위하여 뇌파의 비선형성에 근거하여 바이스펙트럼 분석기법을 도입하고자 한다. 수술환경에서 뇌파의 계측과 분석은 다양한 외부 아티팩트 요소를 감안하여야 한다. 바이스펙트럼 분석은 비선형적 신호의 특성을 추출하는 분석법으로 외부 유입 아티팩트의 유무를 확인 할 수 있어 뇌파에 인입되어 분석에 영향을 끼치는 아티팩트를 효과적으로 제거하는데 기여한다. 이러한 과정을 통해 SEF, MF와 같은 마취심도 파라미터의 실시간 가변성을 감소시킬 수 있었다. 이러한 가변성 감소는 수술현장에서 실시간 활용 가능한 임상 지표서 SEF, MF의 유용성을 제고시켜 줄 수 있을 것이다.

Keywords

References

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