Title :
ε-tube regression: A new method for motion artifact reduction
Author :
Ansari, S. ; Ward, K. ; Najarian, K.
Author_Institution :
Dept. of Comput. Sci., Virginia Commonwealth Univ., Richmond, VA, USA
fDate :
Aug. 30 2011-Sept. 3 2011
Abstract :
This paper introduces a new regression method, called ε-tube regression (ε-TR), for motion artifact reduction in physiological signals. It forms a tube arround the data which leads to an approximation that models only the motion artifact and not the target signal. Moreover, ε-TR prescribes the shape of the approximation using the available information about the motion artifact. The results show that ε-TR can effectively remove the motion artifacts from the impedance signal measured on the arms.
Keywords :
medical signal processing; motion compensation; regression analysis; arms; epsilon tube regression; impedance signal; motion artifact reduction; physiological signals; Approximation algorithms; Approximation methods; Electron tubes; Impedance; Kernel; Optimization; Shape; Artifacts; Regression Analysis; Support Vector Machines;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6090750