Tracking rhythmicity in nonstationary quasi-periodic biomedical signals using adaptive time-varying covariance

Abstract

A time-varying covariance method for detecting and quantifying the evolution of rhythmicity (frequency) in persistently varying quasi-periodic nonstationary signals is presented. The basic method, evaluated using chirp signals, utilizes a shifting window of fixed length. A substantial reduction in estimation bias and variability are obtained by utilizing an adaptive window whose length is dependent on past frequency estimates. The adaptive window yields estimates that are comparable in accuracy to those obtained using high-resolution time–frequency representation but with lower computation requirements and the potential for on-line application. Finally, an example of the application of the method for analyzing a neural recording is also illustrated.