Self-similarity in High Frequency Data and Applications
Brani Vidakovic, Georgia Institute of Technology
Wednesday, October 7, 2009
3:00 p.m., 223 Weber
Measured bio and neuro responses, MRI, NMR spectra, etc, have intrinsic high frequency components and strong persistent serial correlations inhibiting statistical modeling by traditional techniques. In many modeling scenarios the low-frequency-trends are irrelevant and researchers focus on the high-frequency component and its low-dimensional descriptors.
The talk overviews several traditional wavelet-based techniques for assessing the scaling in 1-, 2-, and 3-D data and some novel related techniques that are under ongoing research by the speaker and colleagues from Georgia Institute of Technology and Emory University.
The applications include analysis and modeling of spectral responses in 1H NMR spectroscopy describing metabolic fluctuations in human plasma, prediction of age-related macular degeneration by high frequency pupil diameter measurements, building classifiers by scaling signatures in 3-D MRI data taken from breast cancer patients and healthy controls, detecting vasospasm signatures in EEG data, and filtering turbulent ground-level ozone concentrations.