CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain.
Epilepsy Behav. 2024 May;154:109744. doi: 10.1016/j.yebeh.2024.109744. Epub 2024 Mar 20.
Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary human scrutiny. Significant information may not be evident and is missed, and misinterpretation remains a serious problem. Can we develop an artificial intelligence system to accurately and efficiently classify EEG and even reveal novel information? In this study, deep learning techniques and, in particular, Convolutional Neural Networks, have been used to develop a model (which we have named eDeeplepsy) for distinguishing different brain states in children with epilepsy.
A novel EEG database from a homogenous pediatric population with epileptic spasms beyond infancy was constituted by epileptologists, representing a particularly intriguing seizure type and challenging EEG. The analysis was performed on such samples from long-term video-EEG recordings, previously coded as images showing how different parts of the epileptic brain are distinctly activated during varying states within and around this seizure type.
Results show that not only could eDeeplepsy differentiate ictal from interictal states but also discriminate brain activity between spasms within a cluster from activity away from clusters, usually undifferentiated by visual inspection. Accuracies between 86 % and 94 % were obtained for the proposed use cases.
We present a model for computer-assisted discrimination that can consistently detect subtle differences in the various brain states of children with epileptic spasms, and which can be used in other settings in epilepsy with the purpose of reducing workload and discrepancies or misinterpretations. The research also reveals previously undisclosed information that allows for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. It does so by documenting a different state (interspasms) that indicates a potentially non-standard signal with distinctive epileptogenicity at that period.
尽管取得了进展，但脑电图的分析和解释仍然主要依赖于超级专业医生的视觉检查。考虑到组成脑电图的大量数据，很多细节不可避免地会被普通人忽略。重要信息可能不明显，容易被遗漏，并且仍然存在误解的问题。我们能否开发一种人工智能系统来准确、高效地对脑电图进行分类，甚至揭示新的信息？在这项研究中，深度学习技术，特别是卷积神经网络，已被用于开发一种模型（我们称之为 eDeeplepsy），用于区分癫痫儿童的不同脑状态。
由癫痫专家组成了一个新的来自同质儿科人群的癫痫性痉挛数据库，这是一种特别有趣的癫痫发作类型和具有挑战性的脑电图。分析是在长期视频脑电图记录的样本上进行的，这些样本以前被编码为图像，显示了在这种癫痫发作类型的不同状态下，癫痫大脑的不同部位如何被不同程度地激活。
结果表明，eDeeplepsy 不仅可以区分发作期和发作间期，还可以区分簇内痉挛之间的大脑活动和远离簇的活动，而这些通常通过视觉检查无法区分。对于提出的用例，准确率在 86%到 94%之间。
我们提出了一种计算机辅助分类模型，可以持续检测癫痫性痉挛儿童各种脑状态的细微差异，并可用于癫痫的其他环境中，以减少工作量和差异或误解。该研究还揭示了以前未被发现的信息，使我们能够更好地理解这种特殊癫痫发作类型的病理生理学和演变特征。它通过记录一个不同的状态（发作间期）来实现，该状态表明在该时期存在具有独特致痫性的非标准信号。
