Towards a Behavioral Index of Seizure Susceptibility

描述（由申请人提供）：有大量的临床证据表明睡眠和癫痫之间的相互作用，但关于癫痫发作预测的文献在很大程度上忽视了警惕状态（SOV）对癫痫发作的影响。由于预测错误率高，现有的癫痫发作预测算法（spa）不够准确，无法用于癫痫护理。最近的SPA评估显示，在睡眠、觉醒或一天中的某些阶段，错误的癫痫发作预测更有可能发生。这些观察结果表明：(1)SPA混淆了某些正常的警惕性状态和真正的预警状态（如果它们存在的话），或者(2)SPA测量仅仅跟踪了向更容易发作的正常警惕性状态的过渡。为了研究不同脑状态下癫痫发作的发生机制，需要通过连续的生理测量来准确跟踪SOV的变化。我们开发了SOV识别方法，利用头戴式加速度计的加速度测量来显著改进仅依赖EEG的方法（Sunderam et al.， J. Neurosci.）。方法,2007)。我们建议开发一种实时跟踪SOV的方法：(1)结合使用有监督Fisher判别和无监督贝叶斯判别来建立SOV的地面真值标签；(2)构建状态空间模型（隐马尔可夫模型和切换卡尔曼滤波）来跟踪有噪声的非平稳SOV动态。在当前的项目中，我们将测试这些方法执行准确、实时的大脑状态跟踪的能力，使用慢性脑电图和来自颞叶癫痫的啮齿动物模型以及正常对照的运动学测量。输出将用于量化不同警戒状态下癫痫发作的相对概率。该项目的长期目标是跟踪癫痫患者的警觉性状态，并研究高概率癫痫发作的行为状态。建立癫痫易感性行为指标有助于提高SPAs的性能，并为基于状态的低频电场调制癫痫检测、预测和预防控制奠定基础。这也将有助于测试候选SPA测量是否真正区分癫痫发作前期和正常行为状态。公共卫生相关性：我们的长期目标是开发用于植入式癫痫发作控制装置的方法，用于实时跟踪癫痫患者的警戒状态。某些状态，特别是与睡眠和觉醒有关的状态，更容易导致癫痫发作。癫痫易感性行为指数的可用性将提高现有癫痫预测和控制算法的性能。

DESCRIPTION (provided by applicant): There is overwhelming clinical evidence of the interactions between sleep and epilepsy, but the literature on seizure prediction has largely disregarded the effects of state of vigilance (SOV) on seizure generation. Existing seizure prediction algorithms (SPAs) are not accurate enough for use in epilepsy care due to high false prediction rates. Recent SPA assessments reveal that false seizure predictions are more likely during certain stages of sleep, arousal, or times of day. These observations suggest that (1) SPAs confuse certain normal states of vigilance with truly preictal states (should they exist), or (2) SPA measures merely track transitions to normal states of vigilance that are more susceptible to seizure. In order to investigate the mechanism of seizure generation from different brain states, methods are needed to accurately track changes in SOV from continuous physiological measurements. We have developed SOV discrimination methods that utilize acceleration measurements from head-mounted accelerometers to significantly improve on methods that rely on EEG alone (Sunderam et al., J. Neurosci. Methods, 2007). We propose to develop a methodology for tracking SOV in real time by (1) Applying a combination of supervised Fisher discrimination and unsupervised Bayesian discrimination to establish ground truth labels for SOV; and (2) Constructing state-space models (hidden Markov models and switching Kalman filters) to track the noisy, nonstationary SOV dynamics. In the current project, we will test the ability of these methods to perform accurate, real-time brain state tracking, using chronic EEG and kinematic measurements from a rodent model of temporal lobe epilepsy as well as normal controls. The output will be used to quantify the relative probability of seizure onset from different states of vigilance. The long term goal of this project is to track state of vigilance in epilepsy patients and investigate behavioral states with a high probability of seizure generation. The formulation of a behavioral index of seizure susceptibility will help improve the performance of SPAs, and serve as the basis for state-dependent seizure detection, anticipation and preventive control using low frequency electric field modulation. It will also be useful for testing whether candidate SPA measures truly discriminate between the preseizure period and normal behavioral states. PUBLIC HEALTH RELEVANCE: Our long term goal is to develop, for use in implantable seizure control devices, methods for real-time tracking of state of vigilance in epilepsy patients. Certain states, particularly related to sleep and arousal, are more likely to lead to seizure. The availability of a behavioral index of seizure susceptibility will lead to improved performance of existing seizure prediction and control algorithms.