Application Of EEG In The Assessment Of Disorder Of Consciousness

According to the stability of patient consciousness,disorder of consciousness(DOC)can be divided into unresponsive wakefulness syndrome or vegetative state(UWS/VS)and minimally conscious state(MCS).At present,the clinical level of the patient's behavior is mainly used to assess the patient's level of consciousness.However,behavior-based assessments have high error rates.In recent years,the development of neuroimaging and neurophysiological techniques has brought new dawn to the assessment of the consciousness in patients with DOC.EEG can directly measure a patient's brain activity without the need for active patient involvement,which can improve the accuracy of diagnosis of patients with DOC.Therefore,the use of EEG to identify the state of consciousness of patients with DOC has an important value in clinical application.Changes in neuronal oscillation amplitude across states of consciousness have been widely reported,but little is known about possible changes in the temporal dynamics of these oscillations.To address this question,we investigated long-range temporal correlations of EEG oscillation amplitudes recorded of patient with DOC.The paper uses the detrended fluctuation analysis to extract the feature that can quantify the LRTC of EEG oscillation amplitudes—the scaling exponent.The mean of the scaling exponent of a group of patients shows significantly different in delta,theta,alpha,beta frequency bands in the global.In addition,the scaling exponent of beta frequency band in central brain area significantly correlated with the level of behavioral responsiveness measured using the JFK Coma Recovery Scale Revised(CRS-R).Finally,classifier based on SVM is trained to predict states of consciousness of patients with DOC demonstrated that the scaling exponent of alpha and beta frequency band in central brain area provided the higher classification accuracy(above 80%).Due to the electrical conduction properties of the head and the fact that multiple scalp electrodes,to some extent,collect the activity arising from the same brain sources,these two factors can result in an inaccurate estimation of the real functional connectivity between brain areas.Therefore,the paper proposed to use resting-state EEG source connectivity approaches to study a group of patients with DOC,how the functional connectivity of the brain changes over time.The study found that networks in UWS/VS patients are characterized by impaired global information processing(network integration)and increased local information processing(network segregation)as compared to controls.As the level of consciousness improves,the integration of large-scale brain function networks increases,while the segregation of functional networks decreases.The scaling exponent and the topological parameters of the functional network proposed in the paper provide a new idea for the evaluation of the level of consciousness in clinical application.