DETECTION OF NOISY CHAOS IN A TIME-SERIES

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining wheth er this variability reflects deterministic chaos, or whether it is mer ely ''noise''. The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic co mponents. We present a method that appears to be useful in deciding wh ether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear auto regressive model to the time series followed by an estimation of the c haracteristic exponents of the model over the observed probability dis tribution of states for the system. The method is tested by computer s imulations, and applied to heart rate variability data.