Department of Computer Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
J Neurophysiol. 2013 Sep;110(5):1167-79. doi: 10.1152/jn.01009.2012. Epub 2013 Jun 12.
High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.
高频(100-500Hz)振荡(HFOs)记录自颅内电极是致痫脑的潜在生物标志物。HFOs 通常分为锐波(100-250Hz)或快锐波(250-500Hz),也已确定存在第三类混合频率事件。我们假设 HFO 的时间变化可能可以识别癫痫发作起始可能性增加的时期。使用先前验证的自动算法,对植入混合(微+宏)颅内电极的五名皮质癫痫患者的所有患者的所有通道记录进行检测,从而检测到 HFO(86151)。通过提取定量形态特征对 HFO 进行特征描述,并将其分为四个时间区间(发作间期、发作前期、发作期和发作后期)和三个 HFO 簇(锐波、快锐波和混合事件)。我们使用有监督分类和非参数统计检验来探索发作前后 HFO 特征的定量变化。我们还分析了这些期间每个 HFO 簇中事件的速率和比例的时间变化。我们观察到与锐波、快锐波和混合频率事件的相对速率波动相关的 HFO 形态的患者特异性变化。这些相对速率的变化发生在发作前和发作后期间,可提前三十分钟。我们还发现证据表明,在这些不同时间期间 HFO 的分布在个体患者之间差异很大。这些结果表明,HFO 特征的时间分析具有设计定制癫痫发作预测算法的潜力,并可以探索 HFO 与癫痫发作产生之间的关系。