Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research. On the other hand, building the optimal workstation is a complex task, and system instability can arise from the least obvious sources imaginable. Therefore, the absence of a template for a dedicated seizure prediction workstation in today’s literature is a formidable obstacle to seizure prediction research. To increase the number of researchers working on this problem, a template for a dedicated seizure prediction workstation needs to become available. This paper proposes a novel dedicated system capable of machine learning-based seizure prediction and training for under U.S. $1000, which is significantly less expensive (U.S. $700 or more) than comparable commercial solutions. This powerful workstation will be capable of training sophisticated machine learning algorithms that can be deployed to lightweight wearable devices, which enables the creation of wearable EEG-based seizure early warning systems.

中文翻译：

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使用机器学习和潜在的电子健康应用程序进行癫痫发作预测的新型工作站的概念设计

最近使用机器学习算法处理脑电图 (EEG) 来预测难治性癫痫发作的尝试已显示出巨大的希望。但是，该领域的研究需要专门的工作站。商业解决方案非常昂贵，在大多数国家/地区可能无法使用，并且不是专门为癫痫发作预测研究而设计的。另一方面，构建最佳工作站是一项复杂的任务，系统不稳定可能来自可想象的最不明显的来源。因此，当今文献中缺乏用于专用癫痫发作预测工作站的模板是癫痫发作预测研究的一个巨大障碍。为了增加研究这个问题的研究人员的数量，需要一个专门的癫痫预测工作站的模板。本文提出了一种新颖的专用系统，能够以低于 1000 美元的价格进行基于机器学习的癫痫发作预测和训练，这比类似的商业解决方案要便宜得多（700 美元或更多）。这个强大的工作站将能够训练复杂的机器学习算法，这些算法可以部署到轻型可穿戴设备上，从而能够创建基于可穿戴 EEG 的癫痫预警系统。