Department of Neurological Surgery, Brain Modulation Lab, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Neuroinformatics. 2020 Jun;18(3):365-375. doi: 10.1007/s12021-019-09446-7.
Closed-loop brain stimulation is increasingly used in level 4 epilepsy centers without an understanding of how the device behaves on a daily basis. This lack of insight is a barrier to improving closed-loop therapy and ultimately understanding why some patients never achieve seizure reduction. We aimed to quantify the accuracy of closed-loop seizure detection and stimulation on the RNS device through extrapolating information derived from manually reviewed ECoG recordings and comprehensive device logging information. RNS System event logging data were obtained, reviewed, and analyzed using a custom-built software package. A weighted-means methodology was developed to adjust for bias and incompleteness in event logs and evaluated using Bland-Altman plots and Wilcoxon signed-rank tests to compare adjusted and non-weighted (standard method) results. Twelve patients implanted for a mean of 21.5 (interquartile range 13.5-31) months were reviewed. The mean seizure frequency reduction post-RNS implantation was 40.1% (interquartile range 0-96.2%). Three primary levels of event logging granularity were identified (ECoG recordings: 3.0% complete (interquartile range 0.3-1.8%); Event Lists: 72.9% complete (interquartile range 44.7-99.8%); Activity Logs: 100% complete; completeness measured with respect to Activity Logs). Bland-Altman interpretation confirmed non-equivalence with unpredictable differences in both magnitude and direction. Wilcoxon signed rank tests demonstrated significant (p < 10) differences in accuracy, sensitivity, and specificity at >5% absolute mean difference for extrapolated versus standard results. Device behavior logged by the RNS System should be used in conjunction with careful review of stored ECoG data to extrapolate metrics for detector performance and stimulation.
闭环脑刺激在 4 级癫痫中心越来越多地使用，但人们并不了解设备的日常工作方式。这种缺乏了解是改进闭环治疗的障碍，也是最终理解为什么一些患者从未实现癫痫发作减少的原因。我们旨在通过推断从手动审查的 ECoG 记录和全面的设备日志信息中获得的信息，来量化 RNS 设备上闭环癫痫发作检测和刺激的准确性。使用定制软件包获取、审查和分析 RNS 系统事件日志数据。开发了加权均值方法来调整事件日志中的偏差和不完整性，并使用 Bland-Altman 图和 Wilcoxon 符号秩检验进行评估，以比较调整和非加权（标准方法）的结果。回顾了 12 名平均植入 21.5 个月（四分位距 13.5-31）的患者。RNS 植入后癫痫发作频率的平均减少率为 40.1%（四分位距 0-96.2%）。确定了三种主要的事件日志粒度级别（ECoG 记录：完全 3.0%（四分位距 0.3-1.8%）；事件列表：完全 72.9%（四分位距 44.7-99.8%）；活动日志：100%完全；完全性是相对于活动日志测量的）。Bland-Altman 解释证实了两种方法在幅度和方向上都存在不可预测的差异，且两者不等效。Wilcoxon 符号秩检验表明，对于外推与标准结果的绝对平均差异超过 5%，准确性、灵敏度和特异性存在显著（p < 10）差异。应该结合仔细审查存储的 ECoG 数据，使用 RNS 系统记录的设备行为来推断探测器性能和刺激的度量。
