Clinical trial NCT05949528: Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP
Official title: Assessing Chronic Consciousness Disorders in Patients Using Clinical Evaluation With Resting-state EEG and ERPs: an Extensive Study Exploring Efficacy and Diagnostic Potential of EEG and ERP Measurements
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Disorders of Consciousness
Keywords: resting-state electroencephalograms
ERP
microstate
Lead sponsor: First Affiliated Hospital of Zhejiang University
Sponsor class: OTHER
First posted: 2023-07-18
Enrollment: 200
Sex: ALL
Minimum age (years): 40
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with chronic disorder of consciousness were enrolled in the Hangzhou Mingzhou Brain Rehabilitation Hospital, and healthy controls were all recorded by the First Affiliated Hospital of Zhejiang University.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

1. Patients diagnosed with COMA /VS/MCS
2. The course of disease was more than 4 weeks
3. The vital signs were stable and able to tolerate the test process
4. Complete skull
5. Right-handed, no history of ear disease or hearing loss before onset

Exclusion Criteria:

1. History of epilepsy
2. Sedatives
3. Muscle relaxants and epileptic prophylaxis within 24 hours

Primary outcomes:
measure: Spectrum analysis of chronic disorders of consciousness
timeFrame: 6 months
measure: Duration of each microstate
timeFrame: 6 months
measure: Occurrence of each microstate
timeFrame: 6 months
measure: Global explained variance (GEV) of each microstate
timeFrame: 6 months
Secondary outcomes:
measure: Coma Recovery Scale-Revised(CRS-R)
timeFrame: 30 minutes before samples collection
measure: Glasgow Outcome Scale (GOS)
timeFrame: 6 months
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: Yi Ling
state: Zhejiang
Study officials:
affiliation: Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University
name: Benyuan Luo
role: STUDY_CHAIR
References: