The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients.

中文翻译：

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通过EEG Hjorth参数分析确定抗癫痫药物撤药

对于癫痫发作缓解期延长的患者，继续或停止抗癫痫药物 (AED) 治疗的决定是一个关键问题。以前的研究使用某些风险因素或脑电图 (EEG) 发现来预测 AED 撤出后癫痫发作的复发。然而，缺乏经过验证的生物标志物来指导 AED 的退出。在本研究中，我们使用定量 EEG 分析来建立预测 AED 撤出后癫痫发作复发的方法。将34例癫痫患者分为两组，复发组17例，非复发组17例。所有患者至少两年没有癫痫发作。在 AED 撤出之前，对每位未显示癫痫样放电的患者进行脑电图检查。这些脑电图记录使用基于 Hjorth 参数的脑电图特征进行分类。我们发现复发组患者的 Hjorth 复杂度值高于非复发组患者。极端梯度提升分类方法在准确度、曲线下面积、灵敏度和特异性方面取得了最高的性能（分别为 84.76%、88.77%、89.67% 和 80.47%）。我们提出的方法是一种很有前途的工具，可以帮助医生确定无癫痫发作患者的 AED 撤药情况。和 80.47%，分别）。我们提出的方法是一种很有前途的工具，可以帮助医生确定无癫痫发作患者的 AED 撤药情况。和 80.47%，分别）。我们提出的方法是一种很有前途的工具，可以帮助医生确定无癫痫发作患者的 AED 撤药情况。