Electroencephalograph is a technique of choice for detecting and analyzing various classes of epileptic seizures. In this work, reinforcement learning has been used to proactively classify epileptic seizures in an online manner. In particular, novel online Genetic Algorithm assisted Fuzzy Q-Learning and Fuzzy Q-Learning classifiers have been proposed for epileptic seizures. Proposed Reinforcement Learning based classifier uses Hilbert-Huang Transform to extract 19 time-frequency domain features in the pre-processing stage. Classification accuracy achieved with the proposed Genetic Algorithm assisted Fuzzy Q-Learning and Fuzzy Q-Learning approaches are 96.79% and 93.81%, respectively. This is comparable to the accuracy achieved by other contemporary seizure classification approaches and more accurate than other reinforcement learning based approach. The approach could serve as an effective tool in the hands of medical practitioners for analyzing bulk data and for speeding up seizure diagnosis.

脑电图仪是检测和分析各种类型的癫痫发作的一种选择技术。在这项工作中，强化学习已被用于以在线方式主动分类癫痫发作。特别地，已经提出了用于癫痫发作的新颖的在线遗传算法辅助的模糊Q学习和模糊Q学习分类器。拟议的基于强化学习的分类器使用希尔伯特-黄变换在预处理阶段提取19个时频域特征。提出的遗传算法辅助的模糊Q学习和模糊Q学习方法实现的分类精度分别为96.79％和93.81％。这与其他当代癫痫发作分类方法所达到的准确性相当，并且比其他基于强化学习的方法更为准确。