CMx Lunch

Acquisition of correct features from massive datasets is at the core of data science. A particular interest in medicine is extracting hidden dynamics from a single channel time series composed of multiple oscillatory signals, which could be viewed as a single-channel blind source separation problem. The mathematical and statistical problems are made challenging by the structure of the signal, which consists of non-sinusoidal oscillations, with time varying amplitude/frequency, and by the heteroscedastic nature of the noise. I will discuss recent progress in solving this kind of problem by combining the cepstrum-based nonlinear time-frequency analysis, manifold learning, and random matrix theory. The medical problems motivating this work will be discussed: (1) the extraction of a fetal ECG signal from a single lead maternal abdominal ECG signal; (2) separating respiratory signal from a non-contact PPG signal. If time permits, the clinical trial results and/or an application to the atrial fibrillation will be discussed.