Interdisciplinary Center for Complex Systems
There is a ongoing debate whether generic early warning signals for critical transitions exist that can be applied across diverse systems. The human epileptic brain is often considered as a prototypical system, given the devastating and, at times, even life-threatening nature of the extreme event epileptic seizure. More than three decades of international effort has successfully identified predictors of imminent seizures. However, the suitability of typically applied early warning indicators for critical slowing down, namely variance and lag-1 autocorrelation, for indexing seizure susceptibility is still controversially discussed. Here, we investigated long-term, multichannel recordings of brain dynamics from 28 subjects with epilepsy. Using a surrogate-based evaluation procedure of sensitivity and specificity of time-resolved estimates of early warning indicators, we found no evidence for critical slowing down prior to 105 epileptic seizures.
Researchers from the University of Bonn introduced "circulance," a scalar measure derived from ordinal pattern transition networks, designed to quantify the irregularity of a time series. This method accurately positions periodic, quasiperiodic, chaotic, and stochastic dynamics along a continuous spectrum, and successfully identifies diurnal patterns in human EEG and a dual nature of solar activity in sunspot records.
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