Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

癫痫是一种大脑短暂改变的神经系统疾病。预测癫痫的结果对于提供反馈以促进未来改善结果至关重要。本研究旨在调查对静息态脑电图 (EEG) 信号应用频谱和时间滤波器是否可以改善服用抗癫痫药物治疗颞叶癫痫 (TLE) 患者的结果预测。我们收集了 46 名 TLE 患者的脑电图数据（分为无癫痫发作组（SF，n = 22）和非癫痫发作组（NSF，n = 24）），并回顾性审查了他们的临床数据。我们使用各种时域特征（Hjorth 参数、统计参数、能量、过零率、通道间相关性、通道间锁相值以及从傅里叶变换、斯托克韦尔变换和小波导出的频谱信息）对频谱和时间范围进行分段变换），并通过应用最佳频率策略、最佳持续时间策略和组合策略来比较它们的性能。对于所有时域特征，最佳频率和时间组合策略在区分 SF 患者和 NSF 患者方面表现出最高性能（曲线下面积 (AUC) = 0.790 ± 0.159）。此外，通过利用从 39 至 41 Hz 频带内的统计参数导出的特征向量（窗口长度为 210 秒）实现了最佳性能，AUC 为 0.748。通过确定最佳参数，我们提高了预测模型的性能。这些参数可以作为基于静息态脑电图信号预测结果的标准参数。