Statistical Complexity Analysis of Sleep Stages

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 general...

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Bibliographic Details
Main Authors: Cristina D. Duarte, Marianela Pacheco, Francisco R. Iaconis, Osvaldo A. Rosso, Gustavo Gasaneo, Claudio A. Delrieux
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Entropy
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Online Access:https://www.mdpi.com/1099-4300/27/1/76
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Summary: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.
ISSN:1099-4300