Xiaofeng Wang1,2, Jiayang Sun1 and Kath Bogie1,3
Case Western Reserve University1, The Cleveland Clinic2 and Cleveland FES Center3
Development of medical and computer technology in the last two decades have enabled us to collect huge amounts of data in both spatial and temporal dimensions. Our research is concerned with the spatial-temporal data mining motivated by analyzing data from our "Neuromuscular Electrical Stimulation" experiment. We develop an efficient procedure for mining spatial-temporal data -- Longitudinal Analysis with Self-Registration (LASR, pronounced ``laser''). This new procedure is a statistical ensemble built on following modern or newly developed components:
(1) data segmentation for separating heterogeneous data and for distinguishing outliers,
(2) automatic approaches for spatial and temporal data registration,
(3) statistical smoothing mapping for identifying "activated'' regions based on generalized false discovery rate (FDR) controlled p-maps/movies from "large-p-small-n'' data sets.
Our new procedure should be applicable to other types of spatial-temporal data sets beyond those from the NMES experiment. It has the potential to be used in the analysis of time-series images and functional images such as those from fMRI.
- For our movies and examples, go to the Examples link at the upper right corner.
Acknowledgements:
Reference:
X. Wang, J. Sun, and Kath Bogie (2006), Spatial-Temporal Data Mining Procedure: LASR, IMS LNMS Series, "Recent
Developments in Nonparametric Inference and Probability," Vol 50 (2006),213-231.
Bogie K, Wang X, and Triolo RJ. Long Term Prevention of
Pressure Ulcers in High-risk Individuals: Experience with
NMES. Arch Phys Med Rehabil. 2006; 87(4):585-91.
KM Bogie, RJ Triolo. The effects of regular use of
neuromuscular electrical stimulation on tissue health. J
Rehab Res Dev, 40(6): 469-475, 2003.
Sun and Wang's research is supported in part by two grants from the DMS in NSF awarded to Dr. Sun, and Bogie's research is supported in part by a grant from VA.