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：

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根据高频 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数据低质量、大数据量、高维度的特点，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。这是因为临床医生缺乏选择目标电极的有效标准，而这对于 SOZ 定位非常重要。方法：通过分析长期 SEEG 监测，我们提出了一种用于癫痫手术术前规划的 SOZ 定位方法。考虑到高频振荡可以反映癫痫患者的生理大脑活动，我们首先提取SEEG信号的高频特征，并利用卷积神经网络（CNN）来检测发作间期和癫痫发作段。然后我们提出了一种新的标准，即自适应高频致癫痫指数（AHFEI）来确定目标电极。结果：我们将我们的 SEEG 确定的目标电极与三个成功局灶性癫痫切除手术病例的术前计划进行比较，发现我们的方法的大多数定位结果与临床成功的决策一致，而我们的方法的性能优于国家-最先进的 SOZ 定位方法。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。 临床影响：所提出的自动SOZ定位方法在初步研究中取得了令人满意的性能，具有集成到功能结构融合临床决策系统中的巨大潜力。