Duarte Cristina D, Meo Marcos M, Iaconis Francisco R, Wainselboim Alejandro, Gasaneo Gustavo, Delrieux Claudio
Departamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca 8000, Argentina.
Lingüística y Neurobiología Experimental del Lenguaje (LyNEL), Instituto de Ciencias Humanas, Sociales y Ambientales (INCIHUSA)-Consejo Nacional de Investigaciones Científicas y Técnicas (CCT), Mendoza 5500, Argentina.
Brain Sci. 2025 Jun 27;15(7):691. doi: 10.3390/brainsci15070691.
We present a novel approach for detecting generalized sleep pathologies through the fractal analysis of single-channel electroencephalographic (EEG) signals. We propose that the fractal scaling exponent of permutation entropy time series serves as a robust biomarker of pathological sleep patterns, capturing alterations in brain dynamics across multiple disorders. Using two public datasets (Sleep-EDF and CAP Sleep Database) comprising 200 subjects (112 healthy controls and 88 patients with various sleep pathologies), we computed the fractal scaling of the permutation entropy of these signals. The results demonstrate significantly reduced scaling exponents in pathological sleep compared to healthy controls (mean = 1.24 vs. 1.06, p<0.001), indicating disrupted long-range temporal correlations in neural activity. The method achieved 90% classification accuracy for rapid-eye-movement (REM) sleep behavior disorder (F1-score: 0.89) and maintained 74% accuracy when aggregating all pathologies (insomnia, narcolepsy, sleep-disordered breathing, etc.). The advantages of this approach, including compatibility with single-channel EEG (enabling potential wearable applications), independence from sleep-stage annotations, and generalizability across recording montages and sampling rates, stablish a framework for non-specific sleep pathology detection. This is a computationally efficient method that could transform screening protocols and enable earlier intervention. The robustness of this biomarker could enable straightforward clinical applications for common sleep pathologies as well as diseases associated with neurodegenerative conditions.
我们提出了一种通过对单通道脑电图（EEG）信号进行分形分析来检测全身性睡眠病理的新方法。我们认为，排列熵时间序列的分形缩放指数可作为病理性睡眠模式的可靠生物标志物，捕捉多种疾病中脑动力学的变化。使用两个包含200名受试者（112名健康对照者和88名患有各种睡眠病理的患者）的公共数据集（Sleep-EDF和CAP睡眠数据库），我们计算了这些信号排列熵的分形缩放。结果表明，与健康对照相比，病理性睡眠中的缩放指数显著降低（平均值 = 1.24对1.06，p<0.001），表明神经活动中长程时间相关性被破坏。该方法对快速眼动（REM）睡眠行为障碍的分类准确率达到90%（F1分数：0.89），在汇总所有病理情况（失眠、发作性睡病、睡眠呼吸障碍等）时保持74%的准确率。这种方法的优点包括与单通道EEG兼容（实现潜在的可穿戴应用）、独立于睡眠阶段注释以及在记录导联和采样率方面具有通用性，为非特异性睡眠病理检测建立了一个框架。这是一种计算效率高的方法，可以改变筛查方案并实现早期干预。这种生物标志物的稳健性可以使常见睡眠病理以及与神经退行性疾病相关的疾病的直接临床应用成为可能。
