Quitadamo Lucia R, Foley Elaine, Mai Roberto, de Palma Luca, Specchio Nicola, Seri Stefano
School of Life and Health Sciences, Aston Brain Centre, Aston University, Birmingham, United Kingdom.
Claudio Munari Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy.
Front Neuroinform. 2018 Jul 11;12:45. doi: 10.3389/fninf.2018.00045. eCollection 2018.
The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias, and computational time. In this manuscript, we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space magnetoencephalographic (MEG) data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.
耐药性癫痫患者的术前检查在某些候选患者中需要通过颅内脑电图（iEEG）识别癫痫发作起始区（SOZ），该区域被定义为负责癫痫发作产生的区域，因此是切除的候选区域。iEEG信号中包含的高频振荡（HFOs）已被提议作为SOZ的生物标志物。它们的视觉识别是一个非常繁重的过程，自动化检测工具对于临床医生可能是极其有价值的辅助手段，可减少操作者依赖的偏差和计算时间。在本手稿中，我们展示了EPINETLAB软件，它是作为集成在EEGLAB框架中的一系列例程开发的，旨在为临床医生提供用于HFOs检测和SOZ识别的结构化分析流程。该工具实施了我们团队开发的一种分析策略，并经过了初步临床验证，该策略通过提取HFOs信号的统计特性来识别HFOs区域，并为临床上定义的SOZ与HFO区域之间关系的地形特征提供有用信息。还包括了其他功能，如发作期iEEG数据频谱特性检查以及源空间脑磁图（MEG）数据的导入和分析。EPINETLAB的开发考虑到了用户友好性，以支持临床医生识别和定量评估iEEG和源空间MEG数据中的HFOs，并辅助评估SOZ以进行术前评估。
