avelet minimum description length detrending for ear - infrared spectroscopy

ong Chul Ye orea Advanced Institute of Science and Technology KAIST epartment of Bio and Brain Engineering io Imaging Signal Processing Laboratory 73-1 Guseong-dong useong-gu, Daejeon 305-701 orea Abstract. Near-infrared spectroscopy NIRS can be employed to investigate brain activities associated with regional changes of the oxyand 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 overor underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches. © 2009 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3127204