Prognostic and diagnostic value of EEG signal coupling measures in coma.
Options
BORIS DOI
Publisher DOI
PubMed ID
26578462
Description
OBJECTIVE
Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients.
METHODS
In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians.
RESULTS
Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946).
CONCLUSIONS
EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma.
SIGNIFICANCE
Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.
Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients.
METHODS
In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians.
RESULTS
Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946).
CONCLUSIONS
EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma.
SIGNIFICANCE
Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.
Date of Publication
2015-10-24
Publication Type
Article
Subject(s)
Keyword(s)
Bayes classifier
•
Coma Prognostication
•
Quantitative EEG Synchronization
Language(s)
en
Additional Credits
Series
Clinical neurophysiology
Publisher
Elsevier
ISSN
1388-2457
Access(Rights)
restricted