Clinical Neurophysiology ( IF 4.7 ) Pub Date : 2021-07-01 , DOI: 10.1016/j.clinph.2021.06.011 Ami Kumar 1 , Ekaterina Lyzhko 2 , Laith Hamid 3 , Anand Srivastav 4 , Ulrich Stephani 5 , Natia Japaridze 5
Objective
Childhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.
Methods
Twelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.
Results
Delta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.
Conclusion
Using spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.
Significance
Identifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.
中文翻译:
通过频谱和网络分析区分儿童失神癫痫的发作期/亚临床尖峰和波:一项初步研究
客观的
儿童失神癫痫 (CAE) 是一种具有独特的癫痫发作符号学和脑电图 (EEG) 特征的疾病。区分 EEG 中的发作期和亚临床广义尖峰和波放电 (GSWD) 具有挑战性,因为它们在目视检查时似乎是相同的。在这里,将光谱和功能连接 (FC) 分析应用于 CAE 患者的常规 EEG 数据,以区分发作期和亚临床 GSWD。
方法
本研究回顾性选择了 12 名同时患有发作期和亚临床 GSWD 的 CAE 患者。选定的 EEG 时期在 1-30 Hz 范围内进行频率分析。此外,基于相干性虚部的 FC 分析用于确定传感器级网络。
结果
与亚临床 GSWD 相比,发作期 GSWD 期间的 Delta、alpha 和 beta 频带频率显示出显着更高的功率。FC 显示所有频段的网络差异显着,表明在发作期 GSWD 期间通道之间的连接性较弱。
结论
使用光谱和 FC 分析检测到 CAE 患者发作期和亚临床 GSWD 之间的显着差异,表明这些特征可用于机器学习分类目的,以改善 EEG 监测。
意义
使用常规 EEG 识别发作期和亚临床 GSWD 之间的差异,可以提高对这种综合征的理解和 CAE 患者的管理。