EEG-Based Automatic Classification of ‘Awake’ versus ‘Anesthetized’ State in General Anesthesia Using Granger Causality

Abstract

BackgroundGeneral anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a “cocktail” of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between ‘awake’ and ‘anesthetized’ state during induction and recovery of consciousness under general anesthesia.Methodology/Principal FindingsGranger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of ‘awake’ versus ‘anesthetized’ state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of ‘awake’ and ‘anesthetized’ states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits.Conclusions/SignificanceGC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery.

Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task

“…As a result, the values of ps ranged between 9 and 19 (mean at 12), which are consistent to previous studies (e.g. Barnett and Seth 2011;Keil et al 2009;Nicolaou et al 2012;Protopapa et al 2014).…”

The Role of Dendritic Signaling in the Anesthetic Suppression of Consciousness

“…Several recent studies-carried out in both humans and animals, and using a variety of different anesthetic agents-have investigated differences in directional corticocortical connectivity between the awake state and anesthesia-induced unconsciousness. [12][13][14][15][16][17][18][19][20][21] Intriguingly, the vast majority of these studies [12][13][14]16,[18][19][20][21] (two exceptions 15,17 shall be discussed later in this section) indicate that connectivity along the sensory pathways, under general anesthesia, is reduced primarily in the top-down direction. In other words, unconsciousness, which is the anesthetic endpoint this article is exclusively concerned with, appears to be correlated with reduced signaling from higher-order association cortices to early sensory cortices (table 1 and fig.…”

“…15,17 It is interesting to note that both of these studies, as opposed to those discussed before, used Granger causality to investigate connectivity changes, raising the question of whether methodological differences 19 Humans (n = 30) Baseline electroencephalography Symbolic transfer entropy 20 Humans (n = 10) Baseline electroencephalography Directed phase lag index…”

Automation of anaesthesia: a review on multivariable control

“…Other examples of advanced EEG analysis that may contribute to the problem include Granger causality [28,79], symbolic transfer entropy [52] and permutation entropy [46] [68]. Nonetheless, refinement is necessary for the techniques to be applied in daily practice.…”