Aarabi Ardalan, He Bin
GRAMFC Inserm U1105, University Research Center, University of Picardie-Jules Verne, CHU AMIENS - SITE SUD, Avenue Laennec, 80054 Amiens, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France.
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
Clin Neurophysiol. 2017 Jul;128(7):1299-1307. doi: 10.1016/j.clinph.2017.04.026. Epub 2017 May 12.
We evaluated the performance of our previously developed seizure prediction approach on thirty eight seizures from ten patients with focal hippocampal epilepsy.
The seizure prediction system was developed based on the extraction of correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, largest Lyapunov exponent, and nonlinear interdependence from segments of intracranial EEG.
Our results showed an average sensitivity of 86.7% and 92.9%, an average false prediction rate of 0.126 and 0.096/h, and an average minimum prediction time of 14.3 and 33.3min, respectively, using seizure occurrence periods of 30 and 50min and a seizure prediction horizon of 10s. Two-third of the analyzed seizures showed significantly increased complexity in periods prior to the seizures in comparison with baseline. In four patients, strong bidirectional connectivities between epileptic contacts and the surrounding areas were observed. However, in five patients, unidirectional functional connectivities in preictal periods were observed from remote areas to epileptogenic zones.
Overall, preictal periods in patients with focal hippocampal epilepsy were characterized with patient-specific changes in univariate and bivariate nonlinear measures.
The spatio-temporal characterization of preictal periods may help to better understand the mechanism underlying seizure generation in patients with focal hippocampal epilepsy.
我们评估了我们之前开发的癫痫发作预测方法在10例局灶性海马癫痫患者的38次癫痫发作中的表现。
癫痫发作预测系统基于从颅内脑电图片段中提取关联维数、关联熵、噪声水平、莱姆尔-齐夫复杂度、最大李雅普诺夫指数和非线性相互依存性而开发。
我们的结果显示，使用30分钟和50分钟的癫痫发作发生期以及10秒的癫痫发作预测范围，平均灵敏度分别为86.7%和92.9%，平均误报率分别为0.126和0.096/小时，平均最小预测时间分别为14.3分钟和33.3分钟。三分之二的分析癫痫发作在发作前阶段与基线相比显示出复杂度显著增加。在4例患者中，观察到癫痫病灶与周围区域之间存在强双向连接。然而，在5例患者中，在发作前期观察到从远处区域到癫痫源区的单向功能连接。
总体而言，局灶性海马癫痫患者的发作前期具有单变量和双变量非线性测量中特定于患者的变化特征。
发作前期的时空特征可能有助于更好地理解局灶性海马癫痫患者癫痫发作产生的潜在机制。
