ArticleFrontiers in physiology2022
Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Early Warning Signals in Ecological Time-Series.Entropy (Basel, Switzerland) · 2026Review
- Examining the research methods of early warning signals in clinical psychology through a theoretical lens.BMC psychiatry · 2025Review
- Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data.Sports medicine - open · 2024Article
- Our fragile future under the cumulative cultural evolution of two technologies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation.Health psychology review · 2023Article
- The geometry of synchronization: quantifying the coupling direction of physiological signals of stress between individuals using inter-system recurrence networks.Frontiers in network physiology · 2023Article
- Resilience in sports through the lens of dynamic network structures.Frontiers in network physiology · 2023Article
- Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review.International journal of environmental research and public health · 2022Review
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Abstract
The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.
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