University of Minnesota, Minneapolis, MN 55455, USA; University of Picardie-Jules Verne, France.
University of Minnesota, Minneapolis, MN 55455, USA.
Clin Neurophysiol. 2014 May;125(5):930-40. doi: 10.1016/j.clinph.2013.10.051. Epub 2013 Nov 28.
The aim of this study is to develop a model based seizure prediction method.
A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied.
Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively.
The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.
The present findings suggest that the model-based approach may aid prediction of seizures.
本研究旨在开发一种基于模型的癫痫发作预测方法。
使用神经质量模型来模拟颅内 EEG 数据的宏观动力学。该模型由通过状态方程描述的锥体神经元、兴奋性和抑制性中间神经元组成。通过拟合模型到颅内 EEG 信号的功率谱密度来估计模型的 12 个参数，然后根据在癫痫发作前参数变化的信息进行整合。研究了 21 例药物难治性海马和新皮质局灶性癫痫患者。
在最大敏感性条件下，使用最大癫痫发作期 30 和 50 分钟以及最小癫痫预测潜伏期 10 秒，分别获得平均敏感性 87.07%和 92.6%，平均假预测率为 0.2 和 0.15/h。在最大特异性条件下，系统敏感性降低至 82.9%和 90.05%，最大癫痫发作期 30 和 50 分钟时的假预测率分别降低至 0.16 和 0.12/h。
参数的时空变化显示出特定于患者的发作前特征，可用于癫痫发作预测。
本研究结果表明，基于模型的方法可能有助于预测癫痫发作。
