##### Department of Mathematics,

University of California San Diego

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### Center for Computational Mathematics Seminar

## Peyman Tavallali

#### Caltech

## Adaptive Sparse Time-Frequency Data Analysis and Applications in Cardiovascular Disease Diagnosis

##### Abstract:

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.

Host: Melvin Leok

### June 3, 2014

### 11:00 AM

### AP&M 2402

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