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EEG dynamical correlates of focal and diffuse causes of coma

Overview of attention for article published in BMC Neurology, November 2017
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Title
EEG dynamical correlates of focal and diffuse causes of coma
Published in
BMC Neurology, November 2017
DOI 10.1186/s12883-017-0977-0
Pubmed ID
Authors

MohammadMehdi Kafashan, Shoko Ryu, Mitchell J. Hargis, Osvaldo Laurido-Soto, Debra E. Roberts, Akshay Thontakudi, Lawrence Eisenman, Terrance T. Kummer, ShiNung Ching

Abstract

Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.

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Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 31%
Researcher 5 14%
Student > Master 3 8%
Lecturer 2 6%
Student > Doctoral Student 2 6%
Other 5 14%
Unknown 8 22%
Readers by discipline Count As %
Neuroscience 7 19%
Engineering 5 14%
Medicine and Dentistry 3 8%
Psychology 2 6%
Computer Science 1 3%
Other 4 11%
Unknown 14 39%