Adaptive epileptic seizure prediction system
Adaptive epileptic seizure prediction system
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DOI：
10.1109/tbme.2003.810689
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发表时间：
2003-05-01
影响因子：
4.6
通讯作者：
Tsakalis, K
中科院分区：
文献类型：
--
作者：
Iasemidis, LD;Shiau, DS;Tsakalis, K
目前的癫痫发作“预测”算法通常基于癫痫发作发生时间的知识并回顾性分析脑电图（EEG）记录。很明显，尽管这些分析提供了癫痫发作前大脑活动变化的证据，但它们不能用于开发用于诊断和治疗目的的植入设备。在本文中，我们描述了一种自适应程序，用于在仅知道第一次癫痫发作的发生时间的情况下前瞻性地分析连续的长期脑电图记录。该算法基于自适应选择的关键电极位置之间的短期最大李亚普诺夫指数（STLmax）的收敛和发散。然后发出即将发生扣押的警告。应用全局优化技术来选择电极位点的关键组。适应性癫痫预测算法 (ASPA) 在 5 名难治性颞叶癫痫患者连续 0.76 至 5.84 天的颅内脑电图记录中进行了测试。应用于所有病例的固定参数设置预测了 82% 的癫痫发作，错误预测率为 0.16/h。癫痫发作警告平均出现在发作前 71.7 分钟。通过将可用的脑电图记录分为一半训练和测试部分，产生了类似的结果。优化个体患者的参数提高了灵敏度（总体为 84%）并降低了错误预测率（总体为 0.12/h）。这些结果表明 ASPA 可应用于用于诊断和治疗目的的植入设备。
Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram, (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG, recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.