Department of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, University of Tehran, Tehran, Iran.
IEEE Trans Biomed Eng. 2013 Jul;60(7):1983-92. doi: 10.1109/TBME.2013.2247401. Epub 2013 Feb 14.
In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance.
本文提出了一种快速提取与癫痫发作间期相关的源的方法。该方法基于一般特征值分解,使用两个相关矩阵:1)包含癫痫发作间期放电(IED)的时期,作为参考激活模型;2)不包含 IED 或异常生理信号的时期,作为背景活动。在提取与参考或 IED 状态最相似的源后,通过多目标优化来估计 IED 区域。该方法使用真实模拟数据和患有局灶性癫痫的患者的实际颅内脑电图记录进行了评估。这些患者在切除手术后无癫痫发作。与癫痫学家视觉检查的发作起始区和另一种 IED 区域识别方法进行的定量比较表明,该方法性能良好。