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使用非线性动力学方法（李雅普诺夫指数、莱姆尔-齐夫复杂度和多尺度熵）对结构性局灶性癫痫进行脑电图分析。

EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel-Ziv Complexity, and Multiscale Entropy).

作者信息

Yakovleva Tatiana V, Kutepov Ilya E, Karas Antonina Yu, Yakovlev Nikolai M, Dobriyan Vitalii V, Papkova Irina V, Zhigalov Maxim V, Saltykova Olga A, Krysko Anton V, Yaroshenko Tatiana Yu, Erofeev Nikolai P, Krysko Vadim A

机构信息

Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia.

Medical Center of Neurology, Diagnosis and Treatment of Epilepsy "Epineiro", Saratov 410054, Russia.

出版信息

ScientificWorldJournal. 2020 Feb 11;2020:8407872. doi: 10.1155/2020/8407872. eCollection 2020.

DOI:10.1155/2020/8407872
PMID:32095119
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7036140/
Abstract

This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the "sharp wave" pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been developed for their analysis using nonlinear dynamics tools: calculating the Lyapunov exponent's spectrum, multiscale entropy, and Lempel-Ziv complexity. The calculation of the first Lyapunov exponent is carried out by three methods: Wolf, Rosenstein, and Sano-Sawada, to obtain reliable results. The seven Lyapunov exponent spectra are calculated by the Sano-Sawada method. For the observed patient, studies showed that with medical treatment, his condition did not improve, and as a result, it was recommended to switch from conservative treatment to surgical. The obtained results of the patient's EEG study using the indicated nonlinear dynamics methods are in good agreement with the medical report and MRI data. The approach developed for the analysis of EEG signals by nonlinear dynamics methods can be applied for early detection of structural changes.

摘要

本文通过处理包含大脑活动“尖波”模式的脑电图（EEG）片段，分析了一例患有局灶性结构性癫痫的患者。使用21个通道记录EEG信号。基于EEG信号是时间序列这一事实，开发了一种使用非线性动力学工具对其进行分析的方法：计算李雅普诺夫指数谱、多尺度熵和莱姆尔-齐夫复杂度。通过三种方法计算第一个李雅普诺夫指数：沃尔夫法、罗森斯坦法和佐野-泽田法，以获得可靠结果。通过佐野-泽田法计算七个李雅普诺夫指数谱。对于观察到的患者，研究表明，经过药物治疗，他的病情没有改善，因此建议从保守治疗转向手术治疗。使用上述非线性动力学方法对患者EEG研究获得的结果与医学报告和MRI数据高度吻合。通过非线性动力学方法开发的EEG信号分析方法可用于早期检测结构变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/cedc4b80e4f6/TSWJ2020-8407872.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/9955153d6f0b/TSWJ2020-8407872.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/547b5b1d7a08/TSWJ2020-8407872.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/a9345709a586/TSWJ2020-8407872.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/f195d9dea2f3/TSWJ2020-8407872.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/8ebf6c0813f8/TSWJ2020-8407872.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/24229b29abef/TSWJ2020-8407872.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/252951540593/TSWJ2020-8407872.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/d4cae7627196/TSWJ2020-8407872.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/b90ca838b34f/TSWJ2020-8407872.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/cedc4b80e4f6/TSWJ2020-8407872.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/9955153d6f0b/TSWJ2020-8407872.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/547b5b1d7a08/TSWJ2020-8407872.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/a9345709a586/TSWJ2020-8407872.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/f195d9dea2f3/TSWJ2020-8407872.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/8ebf6c0813f8/TSWJ2020-8407872.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/24229b29abef/TSWJ2020-8407872.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/252951540593/TSWJ2020-8407872.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/d4cae7627196/TSWJ2020-8407872.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/b90ca838b34f/TSWJ2020-8407872.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2225/7036140/cedc4b80e4f6/TSWJ2020-8407872.010.jpg

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