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同步LINNce:癫痫动物模型中神经同步化与去同步化评估工具箱

SynchroLINNce: Toolbox for Neural Synchronization and Desynchronization Assessment in Epilepsy Animal Models.

作者信息

Rodrigues Sofa M A F, Cota Vinícius R

机构信息

Centro Universitário de Lavras, Lavras, Minas Gerais, Brasil. Centro Universitário de Lavras Centro Universitário de Lavras LavrasMinas Brazil.

Rehab Technologies - INAIL Lab, Istituto Italiano di Tecnologia, Genova, Italy. Rehab Technologies - INAIL Lab Istituto Italiano di Tecnologia Genova Italy.

出版信息

Int J Psychol Res (Medellin). 2024 Jul 25;17(2):14-24. doi: 10.21500/20112084.7329. eCollection 2024 Jul-Dec.

Abstract

Epilepsy is a worldwide public health issue, given its biological, social, and economic impacts. Considering several open questions about synchronization and desynchronization mechanisms underlying epileptic phenomena, the development of algorithms and computational toolboxes for such analysis is highly relevant to their research. Moreover, given the recent developments of neurotechnology for epilepsy, it is essential to understand that proposals like computational tools may provide consistent data for closed-loop control systems, necessary in neuromodulation treatment alternatives, and for real-time monitoring systems to predict the occurrence of epileptic seizures. In the present work, SynchroLINNce, a freely distributable MATLAB toolbox designed to be used by epilepsy neuroscientists, including software-untrained), is proposed. Among its features, several functionalities such as recording visualization, digital filtering, and correlation analysis, as well as more specific methodologies, such as mechanisms for the automatic detection of epileptiform spikes, morphology analysis of these spikes, and their coincidence between channels are presented.

摘要

癫痫是一个全球性的公共卫生问题,因其具有生物学、社会和经济影响。鉴于癫痫现象背后的同步和去同步机制存在若干未解决的问题,开发用于此类分析的算法和计算工具箱与它们的研究高度相关。此外,鉴于癫痫神经技术的最新发展,必须认识到像计算工具这样的提议可能为闭环控制系统提供一致的数据,这在神经调节治疗方案中是必要的,并且对于预测癫痫发作发生的实时监测系统也是必要的。在本工作中,提出了SynchroLINNce,这是一个可免费分发的MATLAB工具箱,旨在供癫痫神经科学家(包括未受过软件培训的人员)使用。其功能包括记录可视化、数字滤波和相关性分析等多种功能,以及更具体的方法,如癫痫样棘波的自动检测机制、这些棘波的形态分析及其在通道间的一致性分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/11804118/19a9a0d3c55d/2011-2084-ijpr-17-02-14-gf1.jpg

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