Title: When medical challenges meet time series analysis and manifold learning

Date: 03/17/2017

Time: 4:10 PM - 5:00 PM

Place: 1502 Engineering Building

Speaker: Hau-Tieng Wu, University of Toronto

Adaptive acquisition of correct features from massive datasets
is at the core of modern data analysis. One particular interest in
medicine is the extraction of hidden dynamics from an observed time series
composed of multiple oscillatory signals. The mathematical and statistical
problems are made challenging by the structure of the signal which
consists of non-sinusoidal oscillations with time varying amplitude and
time varying frequency, and by the heteroscedastic nature of the noise. In
this talk, I will discuss recent progress in solving this kind of problem.
Based on the cepstrum-based nonlinear time-frequency analysis and manifold
learning technique, a particular solution will be given along with its
theoretical properties. I will also discuss the application of this method
to two medical problems – (1) the extraction of a fetal ECG signal from a
single lead maternal abdominal ECG signal; (2) the simultaneous extraction
of the instantaneous heart rate and instantaneous respiratory rate from a
PPG signal during exercise. If time permits, an extension to multiple-time
series will be discussed.