  The knowledge of transitions between regular, laminar or chaotic behavior is
essential to understand the underlying mechanisms behind complex systems. While
several linear approaches are often insufficient to describe such processes,
there are several nonlinear methods which however require rather long time
observations. To overcome these difficulties, we propose measures of complexity
based on vertical structures in recurrence plots and apply them to the logistic
map as well as to heart rate variability data. For the logistic map these
measures enable us not only to detect transitions between chaotic and periodic
states, but also to identify laminar states, i.e. chaos-chaos transitions. The
traditional recurrence quantification analysis fails to detect the latter
transitions. Applying our new measures to the heart rate variability data, we
are able to detect and quantify the laminar phases before a life-threatening
cardiac arrhythmia occurs thereby facilitating a prediction of such an event.
Our findings could be of importance for the therapy of malignant cardiac
arrhythmias.
