Exploring non stationary and multivariable time series by Concept and alternating diffusion

Exploring non stationary and multivariable time series by Concept and alternating diffusion

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Hau-Tieng Wu, University of Toronto
Fine Hall 224

Explosive technological advances lead to current and future exponential growth of massive data-sets in medicine and health-related fields. Of particular importance is an adaptive acquisition of suitable intrinsic features and an innovative approach to combine information extracted from multi-modal datasets. For example, the hidden low dimensional physiological dynamics often expresses itself as the time-varying periodicity and trend in the observed dataset. In this talk, I will focus on the non stationary and multivariable time series analysis by combining two adaptive signal processing techniques, alternating diffusion (AD) and concentration of frequency and time (Concept). In addition to the theoretical justification, a direct application to the sleep-cycle and anesthesia depth problem will be demonstrated. If time permits, more applications like photoplethysmography signal analysis will be discussed.