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 () that indicates a potentially non-standard signal with distinctive epileptogenicity at that period.

中文翻译：

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eDeeplepsy：一种人工神经框架，可揭示癫痫痉挛儿童的不同大脑状态

尽管取得了进步，脑电图的分析和解释仍然基本上依赖于超级专业医生的目视检查。考虑到构成脑电图的大量数据，许多细节不可避免地逃脱了普通人类的审查。重要的信息可能不明显并且被遗漏，并且误解仍然是一个严重的问题。我们能否开发出一种人工智能系统来准确有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，被用来开发一个模型（我们将其命名为 eDeeplepsy），用于区分癫痫儿童的不同大脑状态。癫痫学家根据婴儿期以上患有癫痫痉挛的同质儿科人群建立了一个新颖的脑电图数据库，代表了一种特别有趣的癫痫发作类型和具有挑战性的脑电图。这项分析是对来自长期视频脑电图记录的样本进行的，这些样本之前被编码为图像，显示癫痫大脑的不同部分在这种癫痫类型内部和周围的不同状态下如何被明显激活。结果表明，eDeeplepsy 不仅可以区分发作期和发作间期状态，还可以区分簇内痉挛和远离簇的大脑活动，而通常通过目视检查无法区分。对于建议的用例，准确率在 86% 到 94% 之间。我们提出了一种计算机辅助辨别模型，该模型可以一致地检测癫痫痉挛儿童的各种大脑状态的细微差异，并且可以用于癫痫的其他情况，以减少工作量和差异或误解。该研究还揭示了以前未公开的信息，可以更好地了解这种特定癫痫类型的病理生理学和演变特征。它通过记录不同的状态（）来实现这一点，该状态指示该时期具有独特致痫性的潜在非标准信号。