ArticleEntropy (Basel, Switzerland)2025
Statistical Complexity Analysis of Sleep Stages.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns.Brain sciences · 2026Article
- Bifurcations and Hyperchaos in Mathematical Models of Sleep.Bioengineering (Basel, Switzerland) · 2026Review
- Cognitive Processing and EEG Complexity.Entropy (Basel, Switzerland) · 2026Review
- A Permutation Entropy Method for Sleep Disorder Screening.Brain sciences · 2025Article
- Nonlinear Dynamics and Applications.Entropy (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
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. The results highlight the potential of GWPE as a valuable tool for understanding sleep neurophysiology and improving the diagnosis of sleep disorders.
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Registered trials
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