Nonlinear analysis of continuous ECG during sleep II. Dynamical measures

Abstract

The hypothesis that cardiac rhythms are associated with chaotic dynamics implicating a healthy flexibility has motivated the investigation of continuous ECG with methods of nonlinear system theory. Sleep is known to be associated with modulations of the sympathetic and parasympathetic control of cardiac dynamics. Thus, the differentiation of ECG signals recorded during different sleep stages can serve to determine the usefulness of nonlinear measures in discriminating ECG states in general. For this purpose the following six nonlinear measures were implemented: correlation dimension D2, Lyapunov exponent L1. Kolmogorov entropy K2, as well as three measures derived from the analysis of unstable periodic orbits. Results of this study show that continuous ECG signals can be differentiated from linear stochastic surrogates 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 freedom estimated by D2 during REM sleep compared to slow wave sleep. Our findings 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 during REM sleep.

Towards automatic sleep staging via Cross-Recurrence Rate of EEG and ECG activity

“…The increase of CRR values during deeper sleep stages indicates an increase in the similarity of the studied activity. These observations tie in with findings from other studies, where it has been shown that EEG complexity decreases as the sleep deepens and increases again during REM sleep [10], while ECG activity exhibits a similar decrease in complexity during deep sleep compared to REM sleep [16]. Our observations could also be related to changes in the frequency content of both signals during different sleep stages [17].…”

Nonlinear analysis of continuous ECG during sleep I. Reconstruction

“…In conclusion consideration of the ®ndings obtained with the two approaches suggests that embedding dimensions of 6±8 may be regarded as suitable for the topologically proper reconstruction of continuous ECG signals. Based on this knowledge we chose an embedding dimension of 8 in part II of this project (Fell et al 2000), where we investigated how dierent nonlinear characteristics of continuous ECG (correlation dimension, largest Lyapunov exponent, Kolmogorov entropy, as well as several unstable periodic orbit measures) change with sleep stage.…”

“…reconstruction of the signal in a multidimensional space. The choice of the optimal embedding dimension is crucial for proper signal reconstruction and subsequent evaluation of nonlinear characteristics, which we performed in part II of this project (Fell et al 2000). Arti®cial crossing and folding of trajectories in phase space is known to occur in the presence of underembedding and leads to the distortion of phase space structures.…”

Influence of the menstrual cycle on nonlinear properties of heart rate variability in young women

“…Nevertheless, it is difficult to recognize patients with abnormal nonlinear HRV because the nonlinear properties in HRV may be altered under some physiological conditions, and the alterations may be of a sufficient magnitude to confound the so-called alterations derived from diseases. Previous studies have pointed out many physiological factors that affect nonlinear HRV properties, including age (6), gender (35), posture (67), and sleep (19), and so on. Here, we determined the influence of the menstrual cycle on the nonlinear properties of HRV.…”