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• W2162875101 abstract "Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings. Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC. Above proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients’ behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases. Proposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG ( http://braintech.pl/svarog ) and scripts used for creation of the presented profiles (attached to this article)." @default.
• W2162875101 created "2016-06-24" @default.
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• W2162875101 date "2013-01-01" @default.
• W2162875101 modified "2023-10-16" @default.
• W2162875101 title "Electroencephalographic profiles for differentiation of disorders of consciousness" @default.
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• W2162875101 doi "https://doi.org/10.1186/1475-925x-12-109" @default.
• W2162875101 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3819687" @default.
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