Objectives: To quantify the degree of unconsciousness with EEG nonlinear analysis and investigate the change of EEG nonlinear properties under different conditions.
Methods: Twenty-one subjects in persistent vegetative state (PVS), 16 in minimally conscious state (MCS) and 30 normal conscious subjects (control group) with brain trauma or stroke were involved in the study. EEG was recorded under three conditions: eyes closed, auditory stimuli and painful stimuli. EEG nonlinear indices such as Lempel-Ziv complexity (LZC), approximate entropy (ApEn) and cross-approximate entropy (cross-ApEn) were calculated for all the subjects.
Results: The PVS subjects had the lowest nonlinear indices followed by the MCS subjects and the control group had the highest. The PVS and MCS group had poorer response to auditory and painful stimuli than the control group. Under painful stimuli, nonlinear indices of subjects who recovered (REC) increased more significantly than non-REC subjects.
Conclusions: With EEG nonlinear analysis, the degree of suppression for PVS and MCS could be quantified. The changes of brain function for unconscious subjects could be captured by EEG nonlinear analysis.
Significance: EEG nonlinear analysis could characterise the changes of brain function for unconscious state and might have some value in predicting prognosis of unconscious subjects.
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http://dx.doi.org/10.1016/j.clinph.2010.05.036 | DOI Listing |
Brain Sci
December 2024
Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA.
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.
With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states.
View Article and Find Full Text PDFFront Aging Neurosci
January 2025
Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Background: As a clinical precursor to Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) bears a considerably heightened risk of transitioning to AD compared to cognitively normal elders. Early prediction of whether aMCI will progress to AD is of paramount importance, as it can provide pivotal guidance for subsequent clinical interventions in an early and effective manner.
Methods: A total of 107 aMCI cases were enrolled and their electroencephalogram (EEG) data were collected at the time of the initial diagnosis.
Sci Adv
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide.
View Article and Find Full Text PDFChaos
January 2025
Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
Generally, epilepsy is considered as abnormally enhanced neuronal excitability and synchronization. So far, previous studies on the synchronization of epileptic brain networks mainly focused on the synchronization strength, but the synchronization stability has not yet been explored as deserved. In this paper, we propose a novel idea to construct a hypergraph brain network (HGBN) based on phase synchronization.
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