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Proceedings Paper

Wavelet-MDL based detrending method for near infrared spectroscopy (NIRS)
Author(s): Kwang Eun Jang; Sungho Tak; Jaeduck Jang; Jinwook Jung; Jong Chul Ye
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Paper Abstract

Near infrared spectroscopy (NIRS) is a relatively new non-invasive brain imaging method to measure brain activities associated with regional changes of the oxy- and deoxy- hemoglobin concentration. Typically, functional MRI or PET data are analyzed using the general linear model (GLM), in which measurements are modeled as a linear combination of explanatory variables plus an error term. However, the GLM often fails in NIRS if there exists an unknown global trend due to breathing, cardiac, vaso- motion and other experimental errors. In order to overcome these problems, we propose a wavelet-MDL based detrending algorithm. Specifically, the wavelet transform is applied to NIRS measurements to decompose them into global trends, signals and uncorrelated noise components in distinct scales. In order to prevent the over-fitting the minimum length description (MDL) principle is applied. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.

Paper Details

Date Published: 7 March 2008
PDF: 8 pages
Proc. SPIE 6850, Multimodal Biomedical Imaging III, 68500Y (7 March 2008); doi: 10.1117/12.764141
Show Author Affiliations
Kwang Eun Jang, Korea Advanced Institute of Science and Technology (South Korea)
Sungho Tak, Korea Advanced Institute of Science and Technology (South Korea)
Jaeduck Jang, Korea Advanced Institute of Science and Technology (South Korea)
Jinwook Jung, Korea Advanced Institute of Science and Technology (South Korea)
Jong Chul Ye, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 6850:
Multimodal Biomedical Imaging III
Fred S. Azar; Xavier Intes, Editor(s)

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