Recurrence complexity analysis of oscillatory signals with application to general anesthesia EEG signals

Mariia Fedotenkova (1, 2) , Peter Beim Graben (3) , Tamara Tošić (1, 2) , Jamie Sleigh (4) , Axel Hutt (5)

1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
2 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 Bernstein Center for Computational Neuroscience [Berlin]
4 Department of Anaesthesia [Hamilton]
5 DWD - Deutscher Wetterdienst [Offenbach]

Recurrence structures in univariate time series are challenging to detect. We propose a combination of recurrence and symbolic analysis in order to identify such structures in a univariate signal. This method allows to obtain symbolic representation of the signal and quantify it by calculating its complexity measure. To this end, we propose a novel method of phase space reconstruction based on the signal's time-frequency representation and show that the proposed method outperforms conventional phase space reconstruction by delay embedding techniques. We evaluate our method on synthetic data and show its application to experimental EEG signals.

Mots clés: recurrence analysis; recurrence complexity; symbolic dynamics; time-frequency representation; EEG; general anesthesia

Domaines: Neurosciences; Systèmes dynamiques [math.DS]; Traitement du signal et de l'image [eess.SP]

Mariia Fedotenkova, Peter Beim Graben, Tamara Tošić, Jamie Sleigh, Axel Hutt. Recurrence complexity analysis of oscillatory signals with application to general anesthesia EEG signals. 2016. ⟨hal-01343631⟩