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个性化发作前脑电图模式特征分析:时间和定位重要吗?

Personalized preictal EEG pattern characterization: do timing and localization matter?

作者信息

Segal Galya, Keidar Noam, Herskovitz Moshe, Yaniv Yael

机构信息

Laboratory of Bioelectric and Bioenergetic Systems, Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.

Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

出版信息

Front Neurosci. 2025 May 2;19:1526963. doi: 10.3389/fnins.2025.1526963. eCollection 2025.

DOI:10.3389/fnins.2025.1526963
PMID:40386808
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12082717/
Abstract

OBJECTIVES

Better understanding of ictogenesis may allow clinical interventions and potentially reduce the impact of epilepsy on patients' quality of life. This study aims to characterize the EEG changes during the preictal period.

METHODS

This work retrospectively analyzed long-term scalp EEG recordings collected at two neurology centers to characterize preictal activity (start point and duration) for each seizure using EEG features. A channel selection algorithm was implemented and localized preictal activity.

RESULTS

Out of 19 patients, 17 (89.5%) had a distinct preictal pattern, starting 83 ± 60 min before seizure onset and lasting 56 ± 47 min. Spectral Entropy and Hjorth mobility were consistently two out of the three features best distinguished preictal from interictal activity. The third distinguishing feature was either theta power, delta power, beta power, or gamma power. Preictal activity before two seizures in the same patient shared common electrodes and features but differed in duration and timing.

CONCLUSION

Preictal activity, defined as prolonged intervals of uncommon EEG activity, varies in time, localization and signal patterns between individuals and varies in timing and duration between seizures of the same individual.

摘要

目的

更好地理解癫痫发作的起源可能有助于临床干预,并有可能减轻癫痫对患者生活质量的影响。本研究旨在描述发作前期脑电图(EEG)的变化特征。

方法

这项工作回顾性分析了在两个神经科中心收集的长期头皮脑电图记录,以利用脑电图特征描述每次癫痫发作的发作前期活动(起始点和持续时间)。实施了一种通道选择算法并定位发作前期活动。

结果

在19名患者中,17名(89.5%)有明显的发作前期模式,在癫痫发作开始前83±60分钟开始,持续56±47分钟。谱熵和 Hjorth 活动度始终是最能区分发作前期和发作间期活动的三个特征中的两个。第三个区分特征是θ波功率、δ波功率、β波功率或γ波功率。同一患者两次癫痫发作前的发作前期活动共用相同的电极和特征,但持续时间和时间不同。

结论

发作前期活动定义为脑电图活动异常的延长间期,在个体之间的时间、定位和信号模式上有所不同,在同一个体的不同癫痫发作之间的时间和持续时间也有所不同。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/5242bc4602ea/fnins-19-1526963-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/9ef2323adcbe/fnins-19-1526963-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/ff80e469d10e/fnins-19-1526963-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/09edfa7bc8bf/fnins-19-1526963-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/cae80a901a6b/fnins-19-1526963-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/5242bc4602ea/fnins-19-1526963-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/9ef2323adcbe/fnins-19-1526963-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/979afc0279b2/fnins-19-1526963-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/ff80e469d10e/fnins-19-1526963-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/09edfa7bc8bf/fnins-19-1526963-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/cae80a901a6b/fnins-19-1526963-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5461/12082717/5242bc4602ea/fnins-19-1526963-g007.jpg

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Brain Commun. 2024 Sep 20;6(5):fcae328. doi: 10.1093/braincomms/fcae328. eCollection 2024.
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Review: seizure-related consolidation and the network theory of epilepsy.综述:癫痫发作相关的巩固与癫痫网络理论
Front Netw Physiol. 2024 Aug 22;4:1430934. doi: 10.3389/fnetp.2024.1430934. eCollection 2024.
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Learn how to interpret and use intracranial EEG findings.学习如何解读和使用颅内脑电图的发现。
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Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction.利用风险控制预测校准降低癫痫发作预测中的误报率。
Front Neurosci. 2023 Sep 18;17:1184990. doi: 10.3389/fnins.2023.1184990. eCollection 2023.
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Attractor and integrator networks in the brain.大脑中的吸引子网络和整合器网络。
Nat Rev Neurosci. 2022 Dec;23(12):744-766. doi: 10.1038/s41583-022-00642-0. Epub 2022 Nov 3.
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Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset.网络分析表明,癫痫发作前 iEEG 的网络结构发生变化。
Sci Rep. 2022 Jul 22;12(1):12526. doi: 10.1038/s41598-022-16877-x.
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EntropyHub: An open-source toolkit for entropic time series analysis.熵 Hub:用于熵时间序列分析的开源工具包。
PLoS One. 2021 Nov 4;16(11):e0259448. doi: 10.1371/journal.pone.0259448. eCollection 2021.
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Phase Resetting in the Anterior Cingulate Cortex Subserves Childhood Attention and Is Impaired by Epilepsy.扣带前回的相位重置对儿童注意力起作用,且癫痫可使其受损。
Cereb Cortex. 2021 Nov 23;32(1):29-40. doi: 10.1093/cercor/bhab192.
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Long-term seizure dynamics are determined by the nature of seizures and the mutual interactions between them.长期的癫痫动态由癫痫的性质和它们之间的相互作用决定。
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The Role of SPECT and PET in Epilepsy.SPECT 和 PET 在癫痫中的作用。
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