Wickering Ellis, Gaspard Nicolas, Zafar Sahar, Moura Valdery J, Biswal Siddharth, Bechek Sophia, OʼConnor Kathryn, Rosenthal Eric S, Westover M Brandon
*Department of Technical Medicine, University of Twente, Enschede, the Netherlands; †Department of Neurology, Comprehensive Epilepsy Center, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; ‡Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, U.S.A.; and §Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A.
J Clin Neurophysiol. 2016 Jun;33(3):227-34. doi: 10.1097/WNP.0000000000000278.
The purpose of this study is to evaluate automated implementations of continuous EEG monitoring-based detection of delayed cerebral ischemia based on methods used in classical retrospective studies. We studied 95 patients with either Fisher 3 or Hunt Hess 4 to 5 aneurysmal subarachnoid hemorrhage who were admitted to the Neurosciences ICU and underwent continuous EEG monitoring. We implemented several variations of two classical algorithms for automated detection of delayed cerebral ischemia based on decreases in alpha-delta ratio and relative alpha variability. Of 95 patients, 43 (45%) developed delayed cerebral ischemia. Our automated implementation of the classical alpha-delta ratio-based trending method resulted in a sensitivity and specificity (Se,Sp) of (80,27)%, compared with the values of (100,76)% reported in the classic study using similar methods in a nonautomated fashion. Our automated implementation of the classical relative alpha variability-based trending method yielded (Se,Sp) values of (65,43)%, compared with (100,46)% reported in the classic study using nonautomated analysis. Our findings suggest that improved methods to detect decreases in alpha-delta ratio and relative alpha variability are needed before an automated EEG-based early delayed cerebral ischemia detection system is ready for clinical use.
本研究的目的是基于经典回顾性研究中使用的方法，评估基于脑电图（EEG）连续监测自动检测迟发性脑缺血的可行性。我们研究了95例Fisher 3级或Hunt Hess 4至5级的动脉瘤性蛛网膜下腔出血患者，这些患者均入住神经科学重症监护病房（Neurosciences ICU）并接受了连续脑电图监测。我们基于α-δ比值和相对α波变异性的降低，对两种经典算法进行了多种变体，以自动检测迟发性脑缺血。95例患者中，43例（45%）发生了迟发性脑缺血。我们对基于经典α-δ比值的趋势分析方法进行的自动化处理，其敏感性和特异性（Se,Sp）为（80,27）%，而在经典研究中，采用类似方法但非自动化方式报告的数值为（100,76）%。我们对基于经典相对α波变异性的趋势分析方法进行的自动化处理，得到的（Se,Sp）值为（65,43）%，而经典研究采用非自动化分析报告的数值为（100,46）%。我们的研究结果表明，在基于脑电图的早期迟发性脑缺血自动检测系统准备好用于临床之前，需要改进检测α-δ比值和相对α波变异性降低的方法。
