Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy. Methods: We studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient's interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings. Results: GPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This bimodal spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED markup. Conclusion: GPFA events have a characteristic bimodal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS. Significance: This study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For example, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.

中文翻译：

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使用时频分析自动检测发作期脑电图中的阵发性快速活动

目的：在脑电图上标记广泛性发作性癫痫样放电（IED）是癫痫诊断和表征的重要步骤。但是，手动脑电图标记是一项耗时，主观且高度专业化的任务，其中人工检查者需要目视检查大量数据以促进准确的临床决策。这项研究的目的是要开发一种自动检测广义阵发性快速活动（GPFA）的框架，广义阵发性快速活动（GPFA）是Lennox-Gastaut综合征（LGS）患者的头皮脑电图记录中所见的广义IED的特征类型。耐药性全身性癫痫。 方法：我们研究了13名LGS患儿的间质性EEG记录中发生GPFA事件。跨多个EEG通道从手动标记的IED派生的时频信息用于自动检测每个患者的室间隔EEG中的类似事件。我们使用独立的头皮脑电图和同时进行的脑电图功能MRI（EEG-fMRI）记录，验证了所提出的峰值检测方法的正误。 结果：GPFA事件在时频域中显示出一致的低频分量。这种双峰光谱特征在额叶脑电通道上最为突出。我们使用此功能的自动检测方法可识别出可能的癫痫事件，其时频特性与手动标记的GPFA相似。在这些自动检测的IED中，EEG-fMRI信号变化的脑图可与手动IED标记得出的EEG-fMRI脑图相媲美。结论：GPFA事件具有特征性的双峰时频特征，可以从LGS患者的头皮脑电图记录中自动检测出该特征。通过自动检测事件的EEG-fMRI分析证明了这种时频特征的有效性，该分析概括了我们先前显示的LGS中广义IED的脑图。意义：这项研究提供了一种新颖的方法，为LGS中的通用IED的快速，自动化和客观检查铺平了道路。所提出的框架可以扩展到更广泛的癫痫综合症，其中监测癫痫活动的负担可以帮助临床决策。例如，对一般性出院的自动量化可以更快地评估治疗反应并评估未来的癫痫发作风险。