Objective: To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group. Results: Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. Discussion: The MSC could be a useful approach for seizure-monitoring both in the clinic and at home. Methods: Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.

中文翻译：

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临床癫痫发作前头皮脑电图状态的分类

目的：研究提高我们小组先前提出的基于 EEG 的多状态分类器 (MSC) 性能的可行性。结果：在先前报告的患者数据集上使用随机森林 (RF) 分类器，但对分类逻辑进行了三项改进，我们的警报算法的特异性从 82.4% 提高到 92.0%，灵敏度从 87.9% 提高到 95.2%。讨论：MSC 可能是在诊所和家中监测癫痫发作的有用方法。方法：描述了对 MSC 的三项改进。首先，在警报之前使用 RF 输出进行额外检查，以确认癫痫发作状态的可能性增加。其次，实现了说明癫痫发作状态持续时间的报警后检测范围。第三，报警决策窗口保持不变。