Stationarity assessment with time-varying autoregressive modeling

A new method for assessing the stationarity of a signal is addressed. The proposed technique is based on the application of time-varying autoregressive models, in which coefficient variations are decomposed upon a set of deterministic basis functions. Stationarity is evaluated by selecting the optimal number of basis functions with a generalized version of the minimum description length criterion. Results are then validated with hypothesis testing on the model coefficients. Several simulation results are presented. First, application to synthetic signals confirms the basic assumptions and highlights the main features of the method. Second, relevant conclusions are derived for the study of the stationarity of heart rate time series before the onset of ventricular tachyarrhythmias