Differentiating resting brain states using ordinal symbolic analysis

Abstract

Symbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination. Here we use ordinal analysis to investigate the transition between eyes closed (EC) and eyes open (EO) resting states. We analyze two EEG datasets (with 71 and 109 healthy subjects) with different recording conditions (sampling rates and number of electrodes in the scalp). Using as diagnostic tools the permutation entropy, the entropy computed from symbolic transition probabilities, and an asymmetry coefficient (that measures the asymmetry of the likelihood of the transitions between symbols) we show that ordinal analysis applied to the raw data distinguishes the two brain states. In both datasets we find that the EO state is characterized by higher entropies and lower asymmetry coefficient, as compared to the EC state. Our results thus show that these diagnostic tools have potential for detecting and characterizing changes in time-evolving brain states.

Complexity of brain dynamics as a correlate of consciousness in anaesthetized monkeys

“…Our study indicates that the only information quantifier that allows distinguishing between eyes opened or closed in awake states is permutation entropy. This is in agreement with recent work [55], in which it was possible to detect changes of states between eyes open and eyes closed in human scalp electroencephalography using permutation entropy.…”

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

“…In contrast to other methods, PeEn is a time-series complexity measure that is simple to implement, is robust to noise and short time-series, and works for arbitrary data sets. 13,16,17,[20][21][22][23][24][25][26] In particular, it has been shown that PeEn applied to EEG signals captures different states associated with the level of consciousness, both during anesthesia [26][27][28][29] and sleep. 30,31 Hence, in order to study the thalamo-cortical function during W and sleep, PeEn is a practical and reliable method, where results can be understood from primary principles, and can be related to the signal characteristics.…”

Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities

“…Ordinal patterns (OPs) are a symbolic approach to time series analysis that was originally introduced by Bandt and Pompe (2002). Since then, OP based methods have successfully been used in the analyses of biomedical data (Keller et al, 2007b;Amigó et al, 2010Amigó et al, , 2015Parlitz et al, 2012;Graff et al, 2013;Kulp et al, 2016;McCullough et al, 2017) and specifically EEG recordings (Keller et al, 2007a(Keller et al, , 2014Ouyang et al, 2010;Schinkel et al, 2012Schinkel et al, , 2017O'Hora et al, 2013;Rummel et al, 2013;Shalbaf et al, 2015;Unakafov, 2015;Cui et al, 2016;Quintero-Quiroz et al, 2018). Statistics based on ordinal pattern have been shown to be robust to noise (Parlitz et al, 2012;Quintero-Quiroz et al, 2015) and can be used to define advanced concepts for quantifying information flow (Staniek and Lehnertz, 2008;Amigó et al, 2016) or to derive transition networks in state space from observed time series (McCullough et al, 2015;Zhang et al, 2017).…”

“…Therefore, we used ordinal pattern (OP) statistics (Bandt and Pompe, 2002) for characterizing our data which has been shown to be a robust method for analysing physiological time series (Keller et al, 2007a(Keller et al, , 2014Parlitz et al, 2012;Amigó et al, 2015;Unakafov, 2015). For example, OP analysis has been used to separate healthy subjects from patients suffering from congestive heart failure (Parlitz et al, 2012) or to differentiate between different experimental conditions in EEG recordings (Unakafov, 2015;Quintero-Quiroz et al, 2018).…”