Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Method of Feature Extraction on Micro-Raman Spectra for Classification of Neuro-degenerative Disorders

마이크로 라만 스펙트럼에서 퇴행성 뇌신경질환 분류를 위한 특징 추출 방법 연구

  • Park, Aa-Ron (The School of Electronic and Computer Engineering, Chonnam National University) ;
  • Baek, Sung-June (The School of Electronic and Computer Engineering, Chonnam National University)
  • 박아론 (전남대학교 전자컴퓨터공학부) ;
  • 백성준 (전남대학교 전자컴퓨터공학부)
  • Received : 2010.08.30
  • Accepted : 2011.02.18
  • Published : 2011.03.25
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Abstract

Alzheimer's disease and Parkinson's disease are the most common neurodegenerative disorders. In this paper, we proposed a feature extraction method for classification of AD and PD based on micro-Raman spectra from platelet. The first step of the preprocessing is a simple smoothing followed by background elimination to the original spectra to make it easy to measure the intensity of the peaks. The last step of the preprocessing was peak alignment with the reference peak. After the inspection of the preprocessed spectra, we found that proportion of two peak intensity at 743 and $757cm^{-1}$ and peak intensity at 1248 and $1448cm^{-1}$ are the most discriminative features. Then we apply mapstd method for normalization. The method returned data with means to 0 and deviation to 1. With these three features, the classification result involving 263 spectra showed about 95.8% true classification in case of MAP(maximum a posteriori probability).

알츠하이머병(AD: Alzheimer's disease)과 파킨슨병(PD: Parkinson's disease)은 가장 흔한 퇴행성 뇌신경질환이다. 본 연구에서는 라만 스펙트럼을 이용하여 AD와 PD를 분류하기 위해 특징 추출하는 방법을 제안하였다. 혈소판으로부터 측정한 라만 스펙트럼에 먼저 smoothing을 적용한 다음 기준선의 왜곡을 제거하고 스펙트럼의 기준 피크를 중심으로 그 위치를 정렬하는 순서로 이루어진 전처리 과정을 적용하였다. 전처리 과정을 수행한 스펙트럼에서 AD와 PD를 구별할 수 있는 특징을 조사하였고 그 결과 743과 $757cm^{-1}$ 영역의 피크 비와 1248과 $1448cm^{-1}$ 영역의 피크 크기가 가장 변별력 있는 특징임을 확인하였다. 실험 결과에 따르면, 총 216개의 라만 스펙트럼에 대한 MAP(maximum a posteriori probability) 분류 실험에서 이 세 개의 특징만으로도 약 95.8%의 분류율을 보였다.

Keywords

References

  1. R. Brookmeyer, S. Gray and C. Kawas, "Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset," American Journal of Public Health, Vol. 88, No. 9, pp. 1337-1342, Sep. 1998. https://doi.org/10.2105/AJPH.88.9.1337
  2. Department of Economic and Social Affairs, "World population prospects: the 2006 revision, highlights," United Nations, Working Paper No. ESA/P/WP.202, 2007.
  3. R. Brookmeyer, E. Johnson, K. Ziegler and H. Michael Arrighi, "Forecasting the global burden of Alzheimer's disease," The Journal of the Alzheimer's Association, Vol. 3, No. 3, pp. 186-192, Jul. 2007. https://doi.org/10.1016/j.jalz.2007.04.381
  4. J. Jankovic, "Parkinson's disease: clinical features and diagnosis," Journal of Neurology Neurosurgery & Psychiatry, Vol. 78, No. 4, pp. 368-376, Sep. 2007.
  5. P. Tiraboschi, L. Hansen, L. Thai and J. Corey-Bloom, "The importance of neuritic plaques and tangles to the development and evolution of AD," Neurology, Vol. 62, No. 11, pp. 1984-1989, Jun. 2004. https://doi.org/10.1212/01.WNL.0000129697.01779.0A
  6. C. Davie, "A review of Parkinson's disease," British Medical Bulletin, Vol. 86, No. 1, pp. 109-127, Apr. 2008. https://doi.org/10.1093/bmb/ldn013
  7. E. B. Hanlon, R. Manoharan, et al., "Prospect for in vivo Raman spectroscopy," Phys. Med. Biol., Vol. 45, R39-R44, 2000. https://doi.org/10.1088/0031-9155/45/9/102
  8. K. Tang, L. S. hynan, F. Baskin and R. N. Rosenberg, "Platelet amyloid precursor protein processing: A bio-marker for Alzheimer's disease," Journal of the neurological sciences, Vol. 240, pp. 53-58, 2006. https://doi.org/10.1016/j.jns.2005.09.002
  9. P. Chen, A. Shen, W. Zhao, S.-J. Baek, H. Yuan and J. Hu, "Raman signature from brain hippocampus could aid Alzheimer's disease diagnosis," Applied Optics, Vol. 48, No. 24, pp. 4741-4748, Aug. 2009.
  10. Z. W. Cai, F. Xiao, B. Lee, I. A. Paul and P. G. Rhodes, "Prenatal hypoxia-ischemia alters expression activity of nitric oxide synthase in the young rat brain and causes learning deficits," Brain Res. Bull., Vol. 49, pp. 359-365, 1999. https://doi.org/10.1016/S0361-9230(99)00076-3
  11. K. Chiba, L. A. Peterson, K. P. Castagnoli, A J Trevor and N. Castagnoli. "Studies on the molecular mechanism of bioactivation of the selective nigrostriatal toxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine," Drug Metabolism & Disposition, Vol. 13, pp. 342-347, 1985.
  12. W. Dauer and S. Przedborski, "Parkinson's Disease: Mechanisms and Models," Neuron, Vol. 39, pp. 889-909, 2003. https://doi.org/10.1016/S0896-6273(03)00568-3
  13. A. Savitzky and M. J. E. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Analytical Chemistry, Vol. 36, pp. 1627-1639, 1964. https://doi.org/10.1021/ac60214a047
  14. Z. Jianhua, L. Harvey, M. David and Z. Haishan, "Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman Spectroscopy," Society for Applied Spectroscopy, Vol. 61, pp. 248A-270A, Nov. 2007. https://doi.org/10.1366/000370207782597049
  15. S. J. Baek, A. Park, J. Kim, A. Shen, and J. Hu, "A background elimination method based on linear programming for Raman spectra," Chemometrics and Intelligent Laboratory Systems, Vol. 98, No. 1, pp. 24-30, May 2009. https://doi.org/10.1016/j.chemolab.2009.04.007
  16. J. R. Beattie, S. Brockbank, J. J. McGarvey, and W. J. Curry, "Effect of excitation wavelength on the Raman spectroscopy of the porcine photoreceptor layer from the area centralis," Molecular Vision, Vol. 11, pp. 825-832, 2005.
  17. Z. Huang, A. Mcwilliams, H. Lui, D. I. Mclean, S. Lam, and H. Zeng, "Near-infrared Raman Spectroscory for Optical Diagnosis of Lung Cancer," International Journal of Cancer, Vol. 107, pp. 1047-1052, 2003. https://doi.org/10.1002/ijc.11500
  18. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Jone Wiley & Son, Inc. 2001.