Wavelet minimum description length detrending for near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks t...

Full description

Saved in:
Bibliographic Details
Published inJournal of Biomedical Optics Vol. 14; no. 3; pp. 034004 - 0340013
Main Authors Jang, Kwang Eun, Tak, Sungho, Jung, Jinwook, Jang, Jaeduck, Jeong, Yong, Ye, Jong Chul
Format Journal Article
LanguageEnglish
Published United States 01.05.2009
Subjects
Algorithms
Brain - physiology
Brain Mapping - methods
Computer Simulation
detrending
Estimates
Hemodynamics - physiology
Humans
Linear Models
Magnetic Resonance Imaging - methods
Male
Mathematical models
minimum description length principle
Near infrared spectroscopy
Oxyhemoglobins - analysis
Quantitative analysis
Signal processing
Signal Processing, Computer-Assisted
Spectroscopy
Spectroscopy, Near-Infrared - methods
Trends
Wavelet
Wavelet transforms
Online AccessGet full text

Cover

More Information
Summary:Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks to its excellent temporal resolution and flexibility for routine monitoring. Recently, the general linear model (GLM), which is a standard method for functional MRI (fMRI) analysis, has been employed for quantitative analysis of NIRS data. However, the GLM often fails in NIRS when there exists an unknown global trend due to breathing, cardiac, vasomotion, or other experimental errors. We propose a wavelet minimum description length (Wavelet-MDL) detrending algorithm to overcome this problem. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals, and uncorrelated noise components at distinct scales. The minimum description length (MDL) principle plays an important role in preventing over- or underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1083-3668
1560-2281
DOI:10.1117/1.3127204