Under the acceptable hypothesis that cardiac rhythm is an approximately deterministic process with a small scale noise component, an available way is provided to construct a model that can reflect its prominent dynamics of the deterministic component. When applied to the analysis of 19 heart rate data sets, three main findings are stated. The obtained model can reflect prominent dynamics of the deterministic component of cardiac rhythm; cardiac chaos is stated in a reliable way; dynamical noise plays an important role in the generation of complex cardiac rhythm.
In this paper, we first discuss the stability of linearized error dynamics of the nonlinear ob-server used for time-continuous driving chaos synchronization and give the criteria on it. Then we find by theoretical analysis and numerical experiments that the observer can still synchronize with the origi-nal system under time-discrete driving provided that some conditions are met. Finally we derive the asymptotical stability criterion of the nonlinear observer used for time-discrete driving chaos synchro-nization . Simulations illustrate the validity of the criterion.
The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlinear adjusting part placed in front of the linear separation structure is needed to compensate for the distortion in separating such signals. The learning rules for the post nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for blind separation of post nonlinearly mixed sub and super Gaussian signals is proposed based on some previous work. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub and super Gaussian probability models during learning according to a stability condition and operates in a block adaptive manner. The effectiveness of the algorithm is verified by experiments on simulated and real world signals.
