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癫痫领域中用于反应性深部脑刺激和反应性局灶性皮质刺激的关键生物标志物。

Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field.

作者信息

Yu Zhikai, Yang Binghao, Wei Penghu, Xu Hang, Shan Yongzhi, Fan Xiaotong, Zhang Huaqiang, Wang Changming, Wang Jingjing, Yu Shan, Zhao Guoguang

机构信息

Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.

Laboratory of Brain Inspired Intelligence, Capital Medical University, Beijing 100053, China.

出版信息

Fundam Res. 2024 Jun 24;5(1):103-114. doi: 10.1016/j.fmre.2024.05.018. eCollection 2025 Jan.

DOI:10.1016/j.fmre.2024.05.018
PMID:40166115
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11955038/
Abstract

To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.

摘要

从癫痫患者的颅内脑电图中提取关键信号特征，以便为反馈型电刺激系统设计指导。选择去趋势波动分析（DFA）指数作为分类指数，并计算代表不同发作状态的指标之间的差异以及基本机器学习模型的分类效果。DFA指数在发作前期、发作期和发作后期之间表现出统计学上的显著差异。线性判别分析模型在三种基本机器学习模型中表现出最高的准确率，而朴素贝叶斯模型所需的计算和存储空间最少。DFA指数集在机器学习过程中用作中介变量。所得模型具有作为反馈型电刺激系统的反馈触发程序的能力，特别是在癫痫的神经调制领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/2c70e231a2b8/gr9.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/5e404e5ba561/gr3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/02f0fe30fc77/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/5ae8bbc19398/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/e7aa50aa386e/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/2c70e231a2b8/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/40ac4adb605e/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/c53e4bd04e13/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/b9b7ae8fa8b0/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/5e404e5ba561/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/f816cc3f8e45/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/32f23ed14c7c/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/02f0fe30fc77/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/5ae8bbc19398/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/e7aa50aa386e/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51cc/11955038/2c70e231a2b8/gr9.jpg

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Unearthing the mechanisms of responsive neurostimulation for epilepsy.揭示癫痫反应性神经刺激的机制。
Commun Med (Lond). 2023 Nov 16;3(1):166. doi: 10.1038/s43856-023-00401-x.
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A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management.癫痫管理的新兴趋势与创新疗法综合综述
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Expert and deep learning model identification of iEEG seizures and seizure onset times.基于专家和深度学习模型的颅内脑电图癫痫发作及发作起始时间识别
Front Neurosci. 2023 Jul 5;17:1156838. doi: 10.3389/fnins.2023.1156838. eCollection 2023.
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Functional network dynamics between the anterior thalamus and the cortex in deep brain stimulation for epilepsy.深部脑刺激治疗癫痫中前丘脑与皮质之间的功能网络动力学。
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