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Randolph E. Bank
Philip E. Gill
Michael Holst

Administrative Contact:
Terry Le

Office: AP&M 7431
Phone: (858)534-9813
Fax: (858)534-5273
E-mail: tele@ucsd.edu
Adaptive Sparse Time-Frequency Data Analysis and Applications in Cardiovascular Disease Diagnosis

Peyman Tavallali
California Institute of Technology


In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis and monitoring of some cardiovascular diseases.

Tuesday, June 3, 2014
11:00AM AP&M 2402