Duarte Cristina D, Pacheco Marianela, Iaconis Francisco R, Rosso Osvaldo A, Gasaneo Gustavo, Delrieux Claudio A
Departamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, Argentina.
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.
Entropy (Basel). 2025 Jan 16;27(1):76. doi: 10.3390/e27010076.
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.
研究睡眠阶段对于理解睡眠结构至关重要，这有助于识别各种健康状况，包括失眠、睡眠呼吸暂停和神经退行性疾病，从而实现更好的诊断和治疗干预。在本文中，我们探讨了广义加权排列熵（GWPE）在从脑电图信号中区分不同睡眠阶段方面的有效性。使用分类算法，我们评估了从标准排列熵（PE）和GWPE导出的特征集，以确定哪一组在睡眠阶段分类中表现更好，结果表明GWPE显著增强了睡眠阶段的区分能力，特别是在识别N1睡眠和快速眼动（REM）睡眠之间的转换方面。这些结果突出了GWPE作为理解睡眠神经生理学和改善睡眠障碍诊断的宝贵工具的潜力。
