EP 114. Uncovering epileptic seizures – A feasibility study for the semiological analysis of hidden patient motion during epileptic seizures
EP 114. Uncovering epileptic seizures – A feasibility study for the semiological analysis of hidden patient motion during epileptic seizures
复制标题
EP 114 揭示癫痫发作 â 癫痫发作期间隐藏的患者运动的符号学分析的可行性研究
DOI：
10.1016/j.clinph.2016.05.157
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发表时间：
2016
影响因子：
4.7
通讯作者：
S. Noachtar
中科院分区：
文献类型：
--
作者：
F. Achilles;H.M.P. Choupina;A.M. Loesch;J.P.S. Cunha;J. Remi;C. Vollmar;F. Tombari;N. Navab;S. Noachtar
目的通过对癫痫发作的符号学分析，对手术切除的对象进行分类，并为致痫区域的定侧和定位提供有价值的信息。住院患者的癫痫相关运动可以根据视频-脑电记录进行评估，但在癫痫发作期间可以通过例如毯子遮挡、摄像机前的工作人员、图像模糊或如果患者在视野之外来隐藏。基于这一问题，我们提出使用附加在患者肢体上的加速度计传感器，即使在遮挡情况下也可以连续提取运动特征。在这项研究中，我们重点分析发作和发作后运动的分离。方法从15例57次运动性发作患者中，选择6例发作中持续发作和发作后运动的患者作为研究对象。其中一半是在患者完全被毯子覆盖的情况下发生的。加速计(Shimmer)固定在病人的手腕和脚踝上。剩下的三次癫痫发作使患者有了清晰的视野，允许使用基于商业视频的系统(MaxTRAQ)手动跟踪运动。通过视频脑电分析确定所有癫痫发作的发作时相和发作后时相。对手动跟踪数据和加速度计数据进行了对比测量。我们开发了一种新的加速计度量标准，我们称之为敏捷性，它考虑了肢体的旋转和快速移动。敏捷性是加速度向量的绝对变化除以所用时间的总和。结果为了评估这些指标，我们选择了为发作期提供90%敏感性的阈值，并报告了各自的特异性。视频分析在相关工作中，2D中身体部位的轨迹长度成功地区分了额叶和颞叶癫痫的感兴趣运动(MOI)。在本研究中，以[像素]为单位的轨迹长度在发作期为1780±702，在发作期为935±783，特异度为66.7%(阈值为1150)。此外，覆盖的区域以前曾被用来在超级马达和汽车运动癫痫发作中分离MOI。[像素2]这一区域在发作期达到45,160±44,563，在发作后达到16,535±10,702，特异度为61.1%(阈值22,250)。加速度计分析用于超级运动癫痫的检测，目前文献中使用的是加速度的标准差。在3次完全闭塞发作中，发作期[m/S 2]为1.8±0.6，发作后为2.5±0.9，特异度为55.6%(阈值2.8)。新的敏捷度指标在发作期产生3.1±0.9，在发作后阶段产生5.8±3.0，在90%的敏感度下提供66.7%的分离特异度(阈值4.2)，从而与最佳的基于视频的量度相匹配。结论虽然视频跟踪和加速计测量癫痫发作相关运动的不同方面，但它们的准确性是相同的，表明加速度计数据适合于量化发生在毯子下的癫痫发作。有趣的是，基于运动范围的视频指标在发作期较高，而我们根据加速度计数据计算的指标在发作期较高。我们认为对这些互补量化方法的进一步研究是非常有价值的，因为它为连续的定量符号学分析开辟了新的可能性。
Aims Semiological analysis of epileptic seizures is performed to categorize resection candidates and provides valuable information about lateralization and localization of the epileptogenic zone. Seizure related movements of inpatients are evaluated based on video-EEG recordings, but can be hidden during seizures through eg blanket occlusion, staff in front of the camera, image blurring or if the patient is outside of the field of view. Based on this problem, we propose the use of accelerometer sensors attached to the patient extremities, which allows the continuous extraction of motion features, even under occlusion. In this study we focus our analysis on the separation of ictal and postictal movement. Methods From 15 patients with 57 motor seizures, we selected the most suitable patient that consistently performed both ictal and postictal movements in all of his seizures (n= 6). Half of those occurred while the patient was completely covered by the blanket. The accelerometers (Shimmer) were attached to the wrists and ankles of the patient. The remaining three seizures allowed a clear view on the patient, allowing to manually track movements with a commercial video-based system (MaxTRAQ). Ictal and postictal phase of all seizures were determined by video-EEG analysis. Comparative measurements were made for the manual tracking and the accelerometer data. We developed a new metric for accelerometers, which we call agility, accounting for rotation as well as for rapid movement of the extremities. The agility is the sum over absolute changes in the acceleration vector divided by the elapsed time. Results To evaluate the metrics, we chose the threshold that provides 90% sensitivity for the ictal phase and report the respective specificity. Video-analysis In related works, the trajectory-length of a bodypart in 2D successfully distinguished movements of interest (MOIs) in frontal lobe and temporal lobe epilepsy. In this study, the trajectory-length in [pixels] was 1780±702 during ictal and 935±783 during postictal phase, yielding a specificity of 66.7%(threshold 1150). Furthermore, the covered area has previously been used to separate MOIs in hypermotor and automotor seizures. This area in [pixels 2] reached 45,160±44,563 during ictal and 16,535±10,702 during postictal phase with a specificity of 61.1%(threshold 22,250). Accelerometer-analysis For hypermotor seizure detection, the standard deviation of the acceleration was used in current literature. In the three completely occluded seizures, this value in [m/s 2] was 1.8±0.6 during ictal and 2.5±0.9 during postictal phase at a 55.6% specificity (threshold 2.8). The new agility metric in [Hz] yielded 3.1±0.9 during ictal and 5.8±3.0 during postictal phase, providing a separation specificity of 66.7%(threshold 4.2) at 90% sensitivity, thus matching the best video-based metric. Conclusions While video tracking and accelerometers measure different aspects of the seizure related movements, their accuracies are on par, showing that accelerometer data are suitable for the quantification of seizures occurring under a blanket. Interestingly, video metrics based on the range of movements are higher in the ictal phase, while the metrics we calculated from accelerometer data are higher in the postictal phase. We deem further research for these complementary quantification approaches extremely valuable, as it opens up new possibilities for continuous quantitative semiological analysis.