| Autor: |
MohammadMehdi Kafashan, Shoko Ryu, Mitchell J. Hargis, Osvaldo Laurido-Soto, Debra E. Roberts, Akshay Thontakudi, Lawrence Eisenman, Terrance T. Kummer, ShiNung Ching |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
BMC Neurology, Vol 17, Iss 1, Pp 1-11 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2377 |
| DOI: |
10.1186/s12883-017-0977-0 |
| Popis: |
Abstract Background 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. Methods 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. Results 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%. Conclusions 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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