A NOVEL FRAMEWORK FOR SIGNAL REPRESENTATION AND SOURCE SEPARATION: APPLICATIONS TO FILTERING AND SEGMENTATION OF BIOSIGNALS

Abstract

A general technique for representing quasi-periodic oscillations, typical of biomedical signals, is described. Using energy thresholding and Gaussian kernels, in conjunction with a nonlinear gradient descent optimization, it is shown that significant noise reduction, compression and turning point location is possible. As such, the signal representation model can be considered a form of correlated source separation. Applications to filtering, modelling and robust ECG QT-analysis are described.