ArticleJournal of clinical neurophysiology : official publication of the American Electroencephalographic Society2009
Sample entropy tracks changes in electroencephalogram power spectrum with sleep state and aging.
Article in Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 114 citations in OpenAlex.
- Entropy of the resting state cortex in epilepsy.Biomedical physics & engineering express · 2026Article
- Reduced but Reversible Brain Entropy After Occupational Partial Sleep Deprivation in Night-Shift Medical Staff.Brain and behavior · 2026Article
- Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression.GeroScience · 2026Article
- Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.PLoS computational biology · 2026Article
- EEG correlates of sleep stages, sleep discrepancy, and insomnia: a comparative analysis of people with insomnia and healthy sleepers using spectral power and sample entropy.Frontiers in sleep · 2026Article
- A dynamical systems framework for precision psychiatry.NPJ digital medicine · 2025Review
- Complexity of Resting Cortical Activity Predicts Neurophysiological Responses to Theta- Burst Stimulation but Fails to Generalize: A Rigorous Machine-Learning Approach.Research square · 2025Article
- Pumping up your predictive power for cognitive state detection with the proper GAINS.NeuroImage · 2025Article
- Caffeine induces age-dependent increases in brain complexity and criticality during sleep.Communications biology · 2025Article
- Toward an Automatic Classification of the Different Stages of Sleep: Exploring Patterns of Neural Activity in the Subthalamic Nucleus.The European journal of neuroscience · 2025Article
- Neuronal complexity tracks changes of epileptic activity and identifies epilepsy patients independent of interictal epileptiform discharges.Epilepsia · 2025Article
- Impact of interferon-β and dimethyl fumarate on nonlinear dynamical characteristics of electroencephalogram signatures in patients with multiple sclerosis.Frontiers in neuroinformatics · 2025Article
- Unraveling neural complexity: Exploring brain entropy to yield mechanistic insight in neuromodulation therapies for tobacco use disorder.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Information Entropy Measures for Evaluation of Reliability of Deep Neural Network Results.Entropy (Basel, Switzerland) · 2023Article
- Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment.Cognitive neurodynamics · 2022Article
- Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain.Brain connectivity · 2022Article
- Sleep and brain evolution across the human lifespan: A mutual embrace.Frontiers in network physiology · 2022Review
- The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, Alpha Power, Lempel-Ziv complexity and permutation entropy during propofol induction.Frontiers in aging neuroscience · 2022Article
- Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review.Frontiers in systems neuroscience · 2022Review
- Low EEG Gamma Entropy and Glucose Hypometabolism After Corpus Callosotomy Predicts Seizure Outcome After Subsequent Surgery.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The regularity of electroencephalogram signals was compared between middle-aged (47.2 +/- 2.0 years) and elderly (78.4 +/- 3.8 years) female subjects in wake, nonrapid eye movement stages 2 and 3 (S-2, S-3), and rapid eye movement sleep. Signals from C3A2 leads of healthy subjects, acquired from polysomnograms obtained from the Sleep Heart Health Study, were analyzed using both sample entropy (SaEn) and power spectral analysis (delta, theta, alpha, and beta frequency band powers). SaEn changed systematically and significantly (P < 0.001) with sleep state in both age groups, following the relationships wake > rapid eye movement > S-2 > S-3. SaEn was found to be negatively correlated with delta power and positively correlated with beta power. Small changes in SaEn seem to reflect changes in spectral content rather than changes in regularity of the signal. A better predictor of SaEn than the frequency band powers was the logarithm of the power ratio (alpha + beta)/(delta + theta). Thus, SaEn seems to reflect the balance between sleep-promoting and alertness-promoting mechanisms. SaEn of the elderly was larger than that of middle-aged subjects in S-2 (P = 0.029) and rapid eye movement (P = 0.001), suggesting that cortical state is shifted toward alertness in elderly subjects in these sleep states compared with the middle-aged subjects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.