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, 26 (1), 25-30

Automated EEG Entropy Measurements in Coma, Vegetative State/Unresponsive Wakefulness Syndrome and Minimally Conscious State

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Automated EEG Entropy Measurements in Coma, Vegetative State/Unresponsive Wakefulness Syndrome and Minimally Conscious State

Olivia Gosseries et al. Funct Neurol.

Abstract

Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients. Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome. EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome. User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic - albeit not prognostic - tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings.

Figures

Figure 1
Figure 1
Correlation between EEG entropy and Coma Recovery Scale–Revised (CRS-R) behavioral assessments in coma (white squares), acute unresponsive wakefulness syndrome (UWS; black triangles), chronic UWS (black circles), acute minimally conscious state (MCS; grey triangles) and chronic MCS (grey circles). The dotted line shows the EEG entropy cut-off value of 52, separating conscious from unconscious patients. Note that a positive linear correlation between EEG entropy and CRS-R total scores was observed: the higher the EEG entropy value, the higher the level of consciousness. Note also that false positives occurred mainly in chronic cases.
Figure 2
Figure 2
EEG entropy values in acute and chronic disorders of consciousness, and healthy volunteers. (*p<0.05; **p<0.001; ***p<0.0001). Mean EEG entropy values are different between groups. In the acute group, MCS patients showed higher EEG entropy values than VS/UWS patients.
Figure 3
Figure 3
Neuromuscular blocking reduces EEG entropy in a case of post-anoxic encephalopathy.

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