Journal of Korean Ophthalmic Optics Society (한국안광학회지)

The Korean Ophthalmic Optics Society (한국안광학회)
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De-Noising of Electroretinogram Signal Using Wavelet Transforms

웨이브렛 변환을 이용한 망막전도 신호의 잡음제거

  • 서정익 (대구보건대학교 안경광학과) ;
  • 박은규 (대구보건대학교 안경광학과)
  • Received : 2012.05.03
  • Accepted : 2012.06.16
  • Published : 2012.06.30
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Abstract

Purpose: Electroretinogram(ERG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of retinal-related diagnosis with removing signal noise. Methods: Sampling signal was made with generating 60 Hz noise and white noise. The noise were removed using wavelet transforms and bandpass filter. De-noising frequency was compared with Fourier transform spectrum. Removed noises were compared numerically using SNR(signal to noise ratio). Results: The result compared Fourier transform spectrum was showed that 60 Hz noise removed completely and most of white noise was removed by wavelet transforms. 60 Hz and the white noise remained using bandpass filters. The result compared SNR showed that wavelet transforms was 22.8638 and bandpass filter was 4.0961. Conclusions: Wavelet transform showed less signal distortion in removing noise. ERG signal is expected to improve the accuracy of retinal-related diagnosis.

목적: 다른 생체신호와 마찬가지로 망막전도(electroretinogram, ERG) 신호도 측정시 잡음이 발생한다. 이 잡음을 효과적으로 제거하여 망막관련 진단의 정확도를 높이고자 하였다. 방법: ERG 신호에 60 Hz 잡음과 백색잡음을 발생시켜 샘플링 신호를 만들었다. 웨이브렛 변환과 대역통과 필터를 이용하여 잡음를 제거하였다. 푸리에 변환 스펙트럼을 이용하여 제거된 주파수를 비교하였다. 신호대잡음비(signal to noise ratio, SNR)를 이용하여 제거된 잡음을 수치적으로 비교하였다. 결과: 푸리에 변환 스펙트럼을 비교한 결과 웨이브렛 변환에서는 60 Hz 잡음은 완전히 제거 되었으며 백색잡음도 많이 제거되었다. 대역통과필터에서는 60 Hz와 백색잡음 남아 있었다. 신호대잡음비를 비교한 결과에서는 웨이브렛 변환은 22.8638, 대역통과 필터는 4.0961로 나타났다. 결론: 웨이브렛 변환을 이용하여 잡음 제거시 신호의 왜곡을 적게 발생시켜 제거할 수 있었다. 망막전도 신호를 이용한 망막 진단에 정확도를 높일 수 있을 것으로 기대된다.

Keywords

References

  1. Komaromy AM, Brooks DE, Dawson WW, Kallberg ME, Ollivier FJ, Ofri R. Technical issues in electrodiagnostic recording. Veterinary Ophthalmology. 2002;5(2):85-91. https://doi.org/10.1046/j.1463-5224.2002.00229.x
  2. Barraco R, Persano Adorno D, Brai M. ERG signal analysis using wavelet transform. Theory Biosci. 2011;130(3):155-163. https://doi.org/10.1007/s12064-011-0124-1
  3. Alfaouri M, Daqrouq K. ECG signal denoising by wavelet transform thresholding. American Journal of Applied Sciences. 2008;5(3):276-281. https://doi.org/10.3844/ajassp.2008.276.281
  4. Umamaheswara Reddy G, Muralidhar M, Varadarajan S. ECG de-noising using improved threshholding based on wavelet transforms. International Journal of Computer Science and Network Security. 2009;9(9):221-225.
  5. Han JY, Lee SK, Park HB. Denoising ECG using translation invariant mutiwavelet. International Journal of Electrical and Electronics Engineering. 2009;3(3):138-142.
  6. Dewer J. The physiologic action of light. Nature. 1877;15:433-435. https://doi.org/10.1038/015433a0
  7. On YH, An YS. Clinical applications of multifocal electroretinography (mfERG). Journal of Korean Ophthalmological Society. 2002;43(10):1901-1917.
  8. John GW. Medical instrumentation: Application and design, 4th Ed. New Jersey: John Wiley&Sons Inc., 2011;160-162.
  9. Lee SH, Yoon DH. Introduction to the wavelet transform, 2nd Ed. seoul: Jinhan books, 2003:51-53.
  10. Choi CH, Kim YJ, Kim TH, Ahn YH, Shin DR. Information processing and interdisciplinary technology; analysis of QRS-wave using wavelet transform of electrocardiogram. Journal of Biosystems Engineering. 2008;33(5):317-325. https://doi.org/10.5307/JBE.2008.33.5.317
  11. Kang HB, Kim DK, Seo JG. Wavelet theory and its applications. 1st Ed. Seoul: Acanet, 2001;2-6.
  12. Chinchkhede KD, Yadav GS, Hirekhan SR, Solanke DR. On the implementation of FIR filter with various windows for enhancement of ECG signal. International Journal of Engineering Science and Technology. 2011;3(3): 2031-2040.