Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany.
Physics Department, University of Bucharest, Bucharest, Romania.
Epilepsia. 2018 Mar;59(3):650-660. doi: 10.1111/epi.13997. Epub 2018 Jan 11.
Epilepsy is characterized by transient alterations in brain synchronization resulting in seizures with a wide spectrum of manifestations. Seizure severity and risks for patients depend on the evolution and spread of the hypersynchronous discharges. With standard visual inspection and pattern classification, this evolution could not be predicted early on. It is still unclear to what degree the seizure onset zone determines seizure severity. Such information would improve our understanding of ictal epileptic activity and the existing electroencephalogram (EEG)-based warning and intervention systems, providing specific reactions to upcoming seizure types. We investigate the possibility of predicting the future development of an epileptic seizure during the first seconds of recordings after their electrographic onset.
Based on intracranial EEG recordings of 493 ictal events from 26 patients with focal epilepsy, a set of 25 time and frequency domain features was computed using nonoverlapping 1-second time windows, from the first 3, 5, and 10 seconds of ictal EEG. Three random forest classifiers were trained to predict the future evolution of the seizure, distinguishing between subclinical events, focal onset aware and impaired awareness, and focal to bilateral tonic-clonic seizures.
Results show that early seizure type prediction is possible based on a single EEG channel located in the seizure onset zone with correct prediction rates of 76.2 ± 14.5% for distinguishing subclinical electrographic events from clinically manifest seizures, 75 ± 16.8% for distinguishing focal onset seizures that are or are not bilateral tonic-clonic, and 71.4 ± 17.2% for distinguishing between focal onset seizures with or without impaired awareness. All predictions are above the chance level (P < .01).
These findings provide the basis for developing systems for specific early warning of patients and health care providers, and for targeting EEG-based closed-loop intervention approaches to electrographic patterns with a high inherent risk to become clinically manifest.
癫痫的特征是脑同步性的短暂改变，导致发作具有广泛的表现谱。发作的严重程度和患者的风险取决于过度同步放电的演变和传播。通过标准的视觉检查和模式分类，这种演变无法早期预测。发作起始区在多大程度上决定发作的严重程度尚不清楚。这些信息将有助于我们理解发作期癫痫活动以及现有的基于脑电图（EEG）的预警和干预系统，为即将发生的发作类型提供特定的反应。我们研究了在脑电图发作后最初几秒钟记录的癫痫发作的未来发展的预测可能性。
基于 26 例局灶性癫痫患者的 493 例癫痫发作的颅内 EEG 记录，使用非重叠的 1 秒时间窗，从发作性 EEG 的前 3、5 和 10 秒计算了一组 25 个时频域特征。使用三个随机森林分类器来预测发作的未来演变，区分亚临床事件、局灶性起始有意识和无意识、局灶性到双侧强直阵挛发作。
结果表明，基于位于发作起始区的单个 EEG 通道进行早期发作类型预测是可能的，其正确预测率分别为 76.2%±14.5%，用于区分亚临床电描记事件与临床明显发作；75%±16.8%，用于区分局灶性起始发作是否双侧强直阵挛；71.4%±17.2%，用于区分局灶性起始发作是否有意识障碍。所有预测均高于机会水平（P<.01）。
这些发现为开发针对患者和医疗保健提供者的特定早期预警系统以及针对具有高临床表现固有风险的脑电图模式的基于 EEG 的闭环干预方法提供了基础。
