Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.

中文翻译：

---

从高频 SEEG 信号自动定位癫痫发作区：初步研究

目的：立体脑电图（SEEG）已广泛应用于检测癫痫患者的电活动。由于 SEEG 数据质量低、海量、高维的特点，综合利用 SEEG 信号自动精确地确定癫痫发作区 (SOZ) 仍然具有挑战性。这是因为缺乏临床医生选择目标电极的有效标准，这对于 SOZ 定位非常重要。方法：我们通过分析癫痫手术术前计划的长期 SEEG 监测提出了一种 SOZ 定位方法。考虑到高频振荡可以反映癫痫患者的大脑生理活动，我们首先提取 SEEG 信号的高频特征，并利用卷积神经网络 (CNN) 检测发作间期和癫痫发作段。然后我们提出了一个新的标准，即自适应高频致癫痫指数（AHFEI），以确定目标电极。结果：我们将我们的 SEEG 确定的目标电极与成功的局灶性癫痫切除手术病例的三个术前规划进行比较，发现我们方法的大多数定位结果与临床成功决策一致，而我们方法的性能优于状态- SOZ 定位的最先进方法。结论：我们的 SEEG 确定的 SOZ 定位方法可以帮助临床医生根据长期 SEEG 数据自动有效地初步选择潜在的目标电极。