Nonlinear analysis of continuous ECG during sleep II. Dynamical measures

The hypothesis that cardiac rhythms are associated with chaotic dynamics im plicating a healthy flexibility has motivated the investigation of continuo us ECG with methods of nonlinear system theory. Sleep is known to be associ ated with modulations of the sympathetic and parasympathetic control of car diac dynamics. Thus, the differentiation of ECG signals recorded during dif ferent sleep stages can serve to determine the usefulness of nonlinear meas ures in discriminating ECG states in general. For this purpose the followin g six nonlinear measures were implemented: correlation dimension D2, Lyapun ov exponent L1. Kolmogorov entropy K2, as well as three measures derived fr om the analysis of unstable periodic orbits. Results of this study show tha t continuous ECG signals can be differentiated from linear stochastic surro gates by each of the nonlinear measures. The most significant finding with respect to the sleep-related differentiation of ECG signals is an increase in dominant chaoticity assessed by L1 and a reduction in the degrees of fre edom estimated by D2 during REM sleep compared to slow wave sleep, Our find ings suggest that the increase in dominant chaoticity during REM sleep with regard to time-continuous nonlinear analysis is comparable to an increased heart rate variability. The reduction in the correlation dimension may be interpreted as an expression of the withdrawal of respiratory influences du ring REM sleep.