Electroencephalogram/event-related potentials (EEG/ERP) data will be collected from 50 participants in coma or other disorder of consciousness (DOC; i.e., Unresponsive Wakefulness Syndrome \[UWS\] or Minimally Conscious State \[MCS\]), clinically diagnosed using the Glasgow Coma Scale (GCS). For coma patients, EEG recordings will be conducted for up to 24 consecutive hours at a maximum of 5 timepoints, spanning 30 days from the date of recruitment, to track participants' clinical state. For DOC patients, there will be an initial EEG recording up to 24 hours, with possible subsequent weekly recordings up to 2 hours. An additional dataset from 40 healthy controls will be collected, each spanning up to a 12-hour recording period in order to formulate a baseline. Collected data are to form the basis for automatic analysis and detection of ERP components in DOC, using a machine learning paradigm. Salient features (i.e., biomarkers) extracted from the ERPs and resting-state EEG will be identified and combined in an optimal fashion to give an accurate indicator of prognosis.
Age range
18 Years
Sex
ALL
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Change in multiple electrophysiological measures across specified time points during coma
Timeframe: up to 30 days from date of recruitment
Change in multiple electrophysiological measures across specified time points during MCS or UWS
Timeframe: up to 6 months from date of recruitment
Correlation between behavioral and electrophysiological measures after coma/DOC emergence
Timeframe: Within a 30-day time period post recruitment
Sensitivity and specificity of prognostic capabilities of electrophysiological measures
Timeframe: Within a 30-day time period post recruitment
Feasibility of procedure
Timeframe: up to 6 months from date of recruitment