Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP

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

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.

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