Towards a Behavioral Index of Seizure Susceptibility

癫痫易感性行为指数

• 批准号: 8006601
• 负责人: Sridhar Sunderam
• 金额: $ 6.2万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-15 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8006601
• 关键词: Behavioral; Predisposition; Seizures; indexing

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.

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存在相互作用，但有关癫痫发作预测的文献在很大程度上忽视了警惕状态（SOV）对癫痫发作产生的影响。由于错误预测率较高，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时间段更有可能做出错误的癫痫发作预测。这些观察结果表明，(1) SPA 混淆了某些正常的警惕状态与真正的发作前状态（如果存在的话），或者 (2) SPA 措施仅跟踪向更容易癫痫发作的正常警惕状态的转变。为了研究不同大脑状态下癫痫发作的机制，需要通过连续的生理测量准确跟踪 SOV 的变化。我们开发了 SOV 辨别方法，该方法利用头戴式加速度计的加速度测量来显着改进仅依赖 EEG 的方法（Sunderam 等人，J. Neurosci.Methods，2007）。我们建议通过以下方式开发一种实时跟踪 SOV 的方法：（1）应用监督费舍尔判别和无监督贝叶斯判别相结合来建立 SOV 的真实标签； (2) 构建状态空间模型（隐马尔可夫模型和切换卡尔曼滤波器）来跟踪噪声、非平稳 SOV 动态。在当前的项目中，我们将使用颞叶癫痫啮齿动物模型以及正常对照的慢性脑电图和运动学测量来测试这些方法执行准确、实时大脑状态跟踪的能力。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惕状态并调查癫痫发作可能性较高的行为状态。癫痫易感性行为指数的制定将有助于提高 SPA 的性能，并作为使用低频电场调制进行状态依赖性癫痫检测、预测和预防控制的基础。它还有助于测试候选 SPA 措施是否真正区分癫痫发作前阶段和正常行为状态。公共健康相关性：我们的长期目标是开发用于植入式癫痫控制设备的实时跟踪癫痫患者警惕状态的方法。某些状态，特别是与睡眠和觉醒相关的状态，更有可能导致癫痫发作。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。