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基于脑电图的癫痫发作灶定位综述:与脑数据多模态融合的共同点

A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.

作者信息

Tajmirriahi Mahnoosh, Rabbani Hossein

机构信息

Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

出版信息

J Med Signals Sens. 2024 Jul 25;14:19. doi: 10.4103/jmss.jmss_11_24. eCollection 2024.

Abstract

Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.

摘要

意外发作会显著降低癫痫患者的生活质量。发作是由大脑特定区域的过度兴奋和解剖学病变引起的,认知障碍和记忆缺陷是其最常见的伴随影响。除了减少发作的治疗外,涉及脑机接口和神经反馈的医学康复在大多数情况下可以改善局灶性癫痫患者的认知和生活质量,特别是在药物难治性癫痫考虑进行切除性癫痫手术治疗时。癫痫病灶的源估计和精确定位可以改善此类康复和治疗。脑电图(EEG)监测以及发作期/发作间期单光子发射计算机断层扫描(SPECT)成像和结构磁共振成像等多模态无创神经成像技术是癫痫病灶定位的常用方法,并且已经在多种研究中进行了探讨。在本文中,我们回顾了基于脑电图的癫痫病灶定位的最新研究,并讨论了各种方法、它们的优点、局限性和挑战,重点是基于模型的数据处理和机器学习算法。此外,我们研究了被称为多模态脑数据融合的脑电图监测和神经成像技术的联合分析是否有可能提高癫痫病灶定位的精度。为此,我们在基于模型的信号处理框架下,进一步回顾和总结了多源数据处理、融合和分析的关键参数以及挑战,以开发多模态脑数据分析系统。本文有可能成为神经科学研究人员开发基于多模态数据分析的局灶性癫痫脑电图康复系统的宝贵资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501b/11373807/ed4aa08664b8/JMSS-14-19-g001.jpg

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