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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/76 |
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| author | Cristina D. Duarte Marianela Pacheco Francisco R. Iaconis Osvaldo A. Rosso Gustavo Gasaneo Claudio A. Delrieux |
| author_facet | Cristina D. Duarte Marianela Pacheco Francisco R. Iaconis Osvaldo A. Rosso Gustavo Gasaneo Claudio A. Delrieux |
| author_sort | Cristina D. Duarte |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-76cb38794e0845f0834fbe72f3ab0e51 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-76cb38794e0845f0834fbe72f3ab0e512025-01-24T13:31:55ZengMDPI AGEntropy1099-43002025-01-012717610.3390/e27010076Statistical Complexity Analysis of Sleep StagesCristina D. Duarte0Marianela Pacheco1Francisco R. Iaconis2Osvaldo A. Rosso3Gustavo Gasaneo4Claudio A. Delrieux5Departamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, ArgentinaDepartamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, ArgentinaDepartamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, ArgentinaInstituto de Física, Universidade Federal de Alagoas UFAL, Maceió 57072-900, BrazilDepartamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, ArgentinaDepartamento 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, ArgentinaStudying 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.https://www.mdpi.com/1099-4300/27/1/76permutation entropystatistical complexitygeneralized weighted permutation entropysleep stages |
| spellingShingle | Cristina D. Duarte Marianela Pacheco Francisco R. Iaconis Osvaldo A. Rosso Gustavo Gasaneo Claudio A. Delrieux Statistical Complexity Analysis of Sleep Stages Entropy permutation entropy statistical complexity generalized weighted permutation entropy sleep stages |
| title | Statistical Complexity Analysis of Sleep Stages |
| title_full | Statistical Complexity Analysis of Sleep Stages |
| title_fullStr | Statistical Complexity Analysis of Sleep Stages |
| title_full_unstemmed | Statistical Complexity Analysis of Sleep Stages |
| title_short | Statistical Complexity Analysis of Sleep Stages |
| title_sort | statistical complexity analysis of sleep stages |
| topic | permutation entropy statistical complexity generalized weighted permutation entropy sleep stages |
| url | https://www.mdpi.com/1099-4300/27/1/76 |
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