Recent review papers have investigated seizure prediction, creating the possibility of preempting epileptic seizures. Correct seizure prediction can significantly improve the standard of living for the majority of epileptic patients, as the unpredictability of seizures is a major concern for them. Today, the development of algorithms, particularly in the field of machine learning, enables reliable and accurate seizure prediction using desktop computers. However, despite extensive research effort being devoted to developing seizure detection integrated circuits (ICs), dedicated seizure prediction ICs have not been developed yet. We believe that interdisciplinary study of system architecture, analog and digital ICs, and machine learning algorithms can promote the translation of scientific theory to a more realistic intelligent, integrated, and low-power system that can truly improve the standard of living for epileptic patients. This review explores topics ranging from signal acquisition analog circuits to classification algorithms and dedicated digital signal processing circuits for detection and prediction purposes, to provide a comprehensive and useful guideline for the construction, implementation and optimization of wearable and integrated smart seizure prediction systems.

中文翻译：

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迈向智能和完全嵌入式癫痫发作预测引擎：回顾。

最近的评论论文研究了癫痫发作的预测，创造了预防癫痫发作的可能性。正确的癫痫发作预测可以显着提高大多数癫痫患者的生活水平，因为癫痫发作的不可预测性是他们的主要担忧。今天，算法的发展，特别是在机器学习领域，可以使用台式计算机进行可靠和准确的癫痫发作预测。然而，尽管致力于开发癫痫发作检测集成电路 (IC) 的大量研究工作，但尚未开发出专用的癫痫发作预测 IC。我们相信系统架构、模拟和数字 IC 以及机器学习算法的跨学科研究可以促进科学理论向更现实的智能、集成、真正提高癫痫患者生活水平的低功耗系统。这篇综述探讨了从信号采集模拟电路到分类算法和用于检测和预测目的的专用数字信号处理电路的主题，为可穿戴和集成智能癫痫预测系统的构建、实施和优化提供了全面而有用的指南。