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基于发作期立体脑电图的致痫灶定位:脑网络和单通道信号特征分析。

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis.

机构信息

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.

出版信息

Epilepsy Res. 2020 Nov;167:106475. doi: 10.1016/j.eplepsyres.2020.106475. Epub 2020 Sep 22.

Abstract

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.

摘要

准确的致痫区(EZ)定位对于接受癫痫手术后癫痫发作自由的耐药性局灶性癫痫患者至关重要。在这项研究中,分析了 35 名耐药性局灶性癫痫患者的发作期立体脑电图数据。分析了基于偏定向相干的有效网络,并应用灰度共生矩阵提取度变化的时变特征。这些特征与单通道信号时频特征(包括近似熵和线长)结合,基于聚类算法定位 EZ。对于所有无癫痫发作的患者(n = 23),该方法有效识别了临床 EZ 接触点和临床 EZ 阻断点,F1 评分为 62.47%和 72.18%。临床 EZ 阻断点识别的灵敏度为 96.00%,为临床医生的决策提供了信息,促使临床医生关注识别的 EZ 阻断点及其附近的接触点。与无癫痫发作的患者(n = 12)相比,提出的方法确定的 EZ 与临床 EZ 的一致性对于无癫痫发作的患者更差。此外,我们的方法提供的结果优于仅使用脑网络或单通道信号特征。这表明结合这些互补特征可以更准确地定位 EZ。

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