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A screening of Alzheimer's disease using basis synthesis by singular value decomposition from Raman spectra of platelet

혈소판 라만 스펙트럼에서 특이값 분해에 의한 기저 합성을 통한 알츠하이머병 검출

  • Park, Aaron (School of Electronics and Computer Engineering, Chonnam National University) ;
  • Baek, Sung-June (School of Electronics and Computer Engineering, Chonnam National University)
  • 박아론 (전남대학교 전자컴퓨터공학부) ;
  • 백성준 (전남대학교 전자컴퓨터공학부)
  • Received : 2013.03.04
  • Accepted : 2013.05.09
  • Published : 2013.05.31

Abstract

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

Keywords

Alzheimer's disease;Dementia;Pattern recognition;Raman spectroscopy;Singular value decomposition

Acknowledgement

Supported by : 한국연구재단

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