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用于量化复杂动力学的多频熵及其在脑电图数据中的应用

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

作者信息

Niu Yan, Xiang Jie, Gao Kai, Wu Jinglong, Sun Jie, Wang Bin, Ding Runan, Dou Mingliang, Wen Xin, Cui Xiaohong, Zhou Mengni

机构信息

College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China.

Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

出版信息

Entropy (Basel). 2024 Aug 27;26(9):728. doi: 10.3390/e26090728.

DOI:10.3390/e26090728
PMID:39330063
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11431093/
Abstract

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.

摘要

多变量熵算法已被证明在脑电图（EEG）信号的复杂性动态分析中有效，研究人员通常将变量配置为多通道时间序列。然而，尽管现有证据表明不同频率的脑节律之间存在相互作用，但从多频率角度对脑动力学进行复杂量化尚未得到广泛探索。在本研究中，我们提出了一种名为多频率熵（mFreEn）的新算法，增强了现有多变量熵算法的能力，并促进了对不同频段脑节律之间相互作用的复杂性研究。首先，利用模拟数据，我们评估了mFreEn对各种噪声信号、频率和幅度的敏感性，研究了嵌入维度和数据长度等参数的影响，并分析了其抗噪声性能。结果表明，与传统的多变量熵算法相比，mFreEn表现出更高的敏感性和更低的参数依赖性。随后，将mFreEn算法应用于实际EEG数据的分析。我们发现，mFreEn在分析各种脑部疾病的静息态EEG数据时表现出良好的诊断性能。此外，mFreEn对由不同任务刺激诱发的EEG活动表现出良好的分类性能。因此，mFreEn为量化复杂动力学提供了另一个重要视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/38333c5cbd50/entropy-26-00728-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/863a450ed7fe/entropy-26-00728-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/dcb2536f0892/entropy-26-00728-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/b92d94c13409/entropy-26-00728-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/57ba5666f823/entropy-26-00728-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/2b29cb2808b9/entropy-26-00728-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/aa4648d82f59/entropy-26-00728-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/3422d02a9a42/entropy-26-00728-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/cf31e27fccef/entropy-26-00728-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/38333c5cbd50/entropy-26-00728-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/863a450ed7fe/entropy-26-00728-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/dcb2536f0892/entropy-26-00728-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/b92d94c13409/entropy-26-00728-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/57ba5666f823/entropy-26-00728-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/2b29cb2808b9/entropy-26-00728-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/aa4648d82f59/entropy-26-00728-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/3422d02a9a42/entropy-26-00728-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/cf31e27fccef/entropy-26-00728-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a23/11431093/38333c5cbd50/entropy-26-00728-g009.jpg

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Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease.阿尔茨海默病和帕金森病相关神经生理信号的熵和分形分析。
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