Application of nonlinear dynamics analysis in assessing unconsciousness: a preliminary study

Area of Science:

• Neuroscience
• Signal Processing

Background:

• Assessing the depth of unconsciousness in patients with brain trauma or stroke is clinically challenging.
• Electroencephalography (EEG) nonlinear analysis offers a potential method to quantify brain function in altered states of consciousness.

Purpose of the Study:

• To quantify the degree of unconsciousness using EEG nonlinear analysis.
• To investigate changes in EEG nonlinear properties under varying sensory stimulation conditions.
• To explore the potential of EEG nonlinear analysis in predicting prognosis for unconscious patients.

Main Methods:

• EEG data were collected from 21 patients in persistent vegetative state (PVS), 16 in minimally conscious state (MCS), and 30 healthy controls.
• EEG was recorded under conditions of eyes closed, auditory stimuli, and painful stimuli.
• Nonlinear indices, including Lempel-Ziv complexity (LZC), approximate entropy (ApEn), and cross-approximate entropy (cross-ApEn), were calculated.

Main Results:

• Persistent vegetative state (PVS) and minimally conscious state (MCS) groups exhibited significantly lower nonlinear indices compared to controls.
• PVS and MCS groups showed diminished responses to auditory and painful stimuli.
• Nonlinear indices increased more significantly in patients who recovered (REC) compared to non-REC patients under painful stimuli.

Conclusions:

• EEG nonlinear analysis can effectively quantify the degree of brain function suppression in PVS and MCS.
• This analysis method captures dynamic changes in brain function associated with unconscious states.
• EEG nonlinear analysis shows promise for characterizing unconscious states and predicting patient prognosis.