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

BACKGROUND: General 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 FINDINGS: Granger 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/SIGNIFICANCE: GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery

Permutation Entropy for Discriminating ‘Conscious’ and ‘Unconscious’ State in General Anesthesia

Part 14: Medical Applications of AIInternational audienceBrain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to the anesthetist due to the induced muscle paralysis. Therefore, a BCI-based device that monitors the spontaneous brain activity and alerts the anesthetist is an essential addition to routine surgery. In this paper the use of Permutation Entropy (PE) as a feature for ‘conscious’ and ‘unconscious’ brain state classification for a BCI-based anesthesia monitor is investigated. PE is a linear complexity measure that tracks changes in spontaneous brain activity resulting from the administration of anesthetic agents. The overall classification performance for 10 subjects, as assessed with a linear Support Vector Machine, exceeds 95%, indicating that PE is an appropriate feature for such a monitoring device

Quantitative comparison with other methods reported in the literature.

<p>Quantitative comparison with other methods reported in the literature.</p

Statistical significance of linear Vs non-linear classification.

<p>Statistical significance of differences in performance of the different classifiers at loss and recovery of consciousness (LOC and ROC respectively). Classifiers: linear (SVM_L) and nonlinear (SVM_NL) Support Vector Machine, and Linear Discriminant Analysis (LDA). Performance (Perf.) estimated as specificity (SP), sensitivity (SE), and accuracy (Acc). Significance was estimated with one-way ANOVA F-test (α = 0.05; F_crit(1,41) = 4.079), and significant differences are marked with *.</p

Average classification performance (mean ± standard deviation) for LOC (top) and ROC (bottom) conditions.

<p>Average classification performance (mean ± standard deviation) for LOC (top) and ROC (bottom) conditions.</p

Patient-wise average GC values ± standard deviation (error bars).

<p>50-second segments of ‘Awake’ (pre-LOC, post-ROC) and ‘Anesthetized’ (mean GC for post-LOC and pre-ROC) states. (a) GC_LF→LP, (b) GC_RF→LP, (c) GC_LF→RP, and (d) GC_RF→RP. The differences in GC between ‘Awake’ and ‘Anesthetized’ states are statistically significant (ANOVA F-test, α = 0.05, p = 0).</p

Average classification performance for each subject at anesthesia induction (LOC).

1<p>: Patient administered a very small quantity of neuromuscular blocking agent (<4 mg) at induction only to facilitate tracheal intubation.</p>2<p>: Maintenance with sevoflurane.</p><p>Performance estimated with nonlinear and linear Support Vector Machine (SVM_NL and SVM_L respectively), and Linear Discriminant Analysis (LDA). ‘TOTAL’ indicates the average performance over all patients.</p

Statistical significance of LOC Vs ROC classification.

<p>Statistical significance of differences between loss and recovery of consciousness conditions (LOC and ROC respectively). Classifiers: linear (SVM_L) and nonlinear (SVM_NL) Support Vector Machine, and Linear Discriminant Analysis (LDA). Significance was estimated with one-way ANOVA F-test (α = 0.05; F_crit(1,41) = 4.079), and significant differences are marked with *.</p

Granger Causality features for 50-Hz notch filtered and unfiltered data.

<p>A: GC between left frontal and right posterior areas for patient S17; B: GC between right frontal and left posterior areas for patient S1. GC is shown for notch filtered EEG (top panels of A and B) and unfiltered EEG (bottom panels of A and B). Vertical lines denote anesthetic induction (dashed line) and recovery of consciousness (dotted line). The effects of diathermy artifacts on the GC estimates can also be identified as sharp outliers.</p