A novel unsupervised initial pattern recognition algorithm based on wavelet transform and window selection

Author

Wu, Yang ; Macdonald, Jeffrey ; Wheeler, Michael ; Krim, Hamid

Author_Institution

North Carolina State Univ., Raleigh, NC, USA

Volume

2

fYear

2003

fDate

9-12 Nov. 2003

Firstpage

1328

Abstract

This paper presents an unsupervised initial pattern recognition approach to determine spectral regions contributing most to intrinsic clustering patterns in nuclear magnetic resonance (NMR) spectra associated with a given pathological stimulus using a small number of realizations based on wavelet transform coupled with a window selection algorithm. Results of different choices of window size are given. The pattern recognition algorithm proposed in this paper can be used as the first step in metabolomic data analysis followed by future supervised algorithms to refine potential biomarkers characterizing tissue-specific lesions further within these regions.

Keywords

NMR spectroscopy; biological tissues; biomedical NMR; pattern clustering; proton magnetic resonance; wavelet transforms; NMR spectra; biomarker; intrinsic clustering pattern; metabolomic data analysis; nuclear magnetic resonance; pathological stimulus; spectral region; supervised algorithm; tissue lesion; unsupervised initial pattern recognition; wavelet transform; window selection algorithm; window size; Biomarkers; Clustering algorithms; Couplings; Data analysis; Lesions; Metabolomics; Nuclear magnetic resonance; Pathology; Pattern recognition; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on

Print_ISBN

0-7803-8104-1

Type

conf

DOI

10.1109/ACSSC.2003.1292204

Filename

1292204