Towards a Behavioral Index of Seizure Susceptibility

描述（由申请人提供）：有压倒性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略了警惕状态（SOV）对癫痫发作的影响。现有的癫痫发作预测算法（SPA）由于高的错误预测率而不足以准确地用于癫痫护理。最近的SPA评估显示，错误的癫痫发作预测更可能发生在睡眠，觉醒或一天中的某些阶段。这些观察结果表明：（1）SPA混淆了某些正常的警觉状态与真正的发作前状态（如果它们存在的话），或者（2）SPA测量仅跟踪向更容易发作的正常警觉状态的过渡。为了研究癫痫发作产生的机制，从不同的大脑状态，需要的方法来准确地跟踪变化的SOV从连续的生理测量。我们已经开发了SOV辨别方法，其利用来自头戴式加速度计的加速度测量来显著改进仅依赖于EEG的方法（Sunderam等人，J. Neurosci.方法，2007）。我们建议开发一种方法来跟踪SOV在真实的时间（1）应用相结合的监督Fisher歧视和无监督贝叶斯歧视，以建立地面真理标签SOV;（2）构建状态空间模型（隐马尔可夫模型和切换卡尔曼滤波器）来跟踪噪声，非平稳SOV动态。在目前的项目中，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EEG和运动测量从颞叶癫痫的啮齿动物模型以及正常对照。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状态，并调查癫痫发作发生概率高的行为状态。癫痫发作易感性的行为指标的制定将有助于提高SPA的性能，并作为使用低频电场调制的状态依赖性癫痫发作检测，预测和预防性控制的基础。它也将是有用的测试候选SPA措施是否真正区分preeizure期间和正常的行为状态。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设备的方法，用于实时跟踪癫痫患者的警惕状态。某些状态，特别是与睡眠和觉醒有关的状态，更有可能导致癫痫发作。癫痫发作易感性的行为指数的可用性将导致现有癫痫发作预测和控制算法的性能改善。

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.