2000 Volume 120 Issue 8-9 Pages 1076-1085
We analyze respiration time series of newborn infants with apnea. At first, stationarity, determinism and predictability of the time series are investigated. The local stationarity decreases in regions where the amplitude suddenly varies as typically observed in apnea, and it is difficult to predict the time series in those regions. Thus, we focus on the characteristics of the decrease in stationarity when apnea occurs, and we propose a new method to construct time series of the fluctuation of the local stationarity from observed data. This time series is called time series of stationarity rates in this paper. In advance of applying this method to the respiration time series, we construct the time series of stationarity rates of numerical data generated by deterministic systems while slowly varying a parameter caused by another deterministic driving force, and we apply the surrogate-data method to demonstrate that the constructed time series can be characterized as deterministic. We apply these techniques to the time series of stationarity rates of the respiration data. Our results suggest that the constructed time series of stationarity rates is possibly deterministic in short term.