Currently, there are significant challenges in the clinical assessment of patients with consciousness disorders, such as distinguishing between vegetative state (VS) and minimally conscious state (MCS), and predicting patient prognosis. This study aims to utilize different research techniques, such as auditory stimulation, as well as modified microstate methods, to enhance the disease classification and prognosis prediction of patients with chronic consciousness disorders.
The investigators collected resting-state electroencephalograms (EEGs) and EEGs under various event-related potential (ERP) stimuli from patients with chronic consciousness disorders, and performed analyses on these data. The resting-state EEGs were subjected to spectral analysis and microstate analysis. The ERP EEGs were analyzed in the time domain, as well as for phase coupling and other measures.Using these computed indicators, the investigators use machine learning, deep learning, and other methods to predict disease classification and prognosis assessment in patients with chronic consciousness disorders.
Study Type
OBSERVATIONAL
Enrollment
200
no intervention
Yi Ling
Hangzhou, Zhejiang, China
RECRUITINGSpectrum analysis of chronic disorders of consciousness
The EEG of 59 patients with disturbance of consciousness will be collected in resting state and listening to music, and the absolute power spectral density values (alpha,beta,theta,delta bands dB/Hz) will be calculated using spectral analysis.
Time frame: 6 months
Duration of each microstate
The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate method to accurately estimate topographical differences. The calculations were performed for measures of duration (ms). The duration of each microstate were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.
Time frame: 6 months
Occurrence of each microstate
The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of occurrence (times per minute). The occurrence of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.
Time frame: 6 months
Global explained variance (GEV) of each microstate
The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of GEV (%). The GEV of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.
Time frame: 6 months
Coma Recovery Scale-Revised(CRS-R)
The Coma Recovery Scale-Revised (CRS-R) score was utilized to measure the severity of the condition. It comprises 23 items arranged hierarchically into six subscales, including auditory, visual, motor, oromotor/verbal, communication, and arousal processes. Reflexive activity is represented by the lowest item on each subscale, while cognitively mediated behaviors are portrayed by the highest items. The scale ranges from 0 (indicating the lowest level of consciousness) to 23 (indicating the highest level of consciousness). Generally, a higher score suggests a better level of consciousness, while a lower score suggests a lower level of consciousness.
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Time frame: 30 minutes before samples collection
Glasgow Outcome Scale (GOS)
A GOS score ≥ 4 points is considered to indicate a good prognosis, while a GOS score \< 4 points is considered to indicate a poor prognosis
Time frame: 6 months