Purpose: Using quantitative EEG (qEEG) analysis, we investigated sleep EEG microstructure as correlates of neurobehavioural performance after 24 h of extended wakefulness in untreated OSA.
Methods: Eight male OSA patients underwent overnight polysomnography (PSG) at baseline followed by 40 h awake with repeated performance testing (psychomotor vigilance task [PVT] and AusEd driving simulator). EEG slowing during REM and spindle density during NREM sleep were calculated using power spectral analysis and a spindle detection algorithm at frontal and central electrode sites. Correlations between sleep EEG microstructure measures and performance after 24-h awake were assessed.
Results: Greater EEG slowing during REM sleep was associated with slower PVT reaction times (rho = - 0.79, p = 0.02), more PVT lapses (rho = 0.87, p = 0.005) and more AusEd crashes (rho = 0.73, p = 0.04). Decreased spindle density in NREM sleep was also associated with slower PVT reaction times (rho = 0.89, p = 0.007). Traditional PSG measures of disease severity were not consistent correlates of neurobehavioural performance in OSA.
Conclusions: Sleep EEG microstructure measures recorded during routine PSG are associated with impaired vigilance in OSA patients after sleep deprivation.
Significance: Quantitative brain oscillatory (or EEG)-based measures of sleep may better reflect the deleterious effects of untreated OSA than traditional PSG metrics in at-risk individuals. Trial Registration ACTRN12606000066583.
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Entropy (Basel)
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Departamento de Ingeniería Eléctrica y Computadoras, Instituto de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, Argentina.
Studying sleep stages is crucial for understanding sleep architecture, which can help identify various health conditions, including insomnia, sleep apnea, and neurodegenerative diseases, allowing for better diagnosis and treatment interventions. In this paper, we explore the effectiveness of generalized weighted permutation entropy (GWPE) in distinguishing between different sleep stages from EEG signals. Using classification algorithms, we evaluate feature sets derived from both standard permutation entropy (PE) and GWPE to determine which set performs better in classifying sleep stages, demonstrating that GWPE significantly enhances sleep stage differentiation, particularly in identifying the transition between N1 and REM sleep.
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Department of Physical Education, Tongji University, Shanghai, 200000, China.
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December 2024
Institute of Physics, Saratov State University, Astrahanskaia, 83, Saratov 410012, Russia.
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View Article and Find Full Text PDFHum Brain Mapp
February 2025
Neuroscience and Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Implicit motor learning involves the acquisition and consolidation of motor skills without conscious awareness, influenced by various factors. Punishment and reward have been identified as significant modulators during training, impacting skill acquisition differently. Additionally, the role of a second declarative task in offline consolidation has been explored, affecting both stabilization and enhancement processes during wake and sleep periods.
View Article and Find Full Text PDFInt J Sport Nutr Exerc Metab
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Centre for Sport Research, Institute of Physical Activity and Nutrition, Deakin University, Geelong, Australia.
Evening consumption of a whey protein rich in the amino acid tryptophan, alpha-lactalbumin (ALAC), has previously shown to benefit sleep-particularly among poor sleepers. Given trained populations often experience sleep difficulty, this study investigated whether evening supplementation of ALAC would influence sleep outcomes, mood, and next-day cognitive performance within a trained population with sleep difficulties. Nineteen trained participants (females, n = 11) with sleep difficulties (Athlete Sleep Screening Questionnaire: 8.
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