Towards a Behavioral Index of Seizure Susceptibility

癫痫易感性行为指数

基本信息

批准号：
7845554
负责人：
Sridhar Sunderam
金额：
$ 7.43万
依托单位：
UNIVERSITY OF KENTUCKY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-15 至 2012-04-30
项目状态：
已结题

项目摘要

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）：有大量的临床证据表明睡眠与癫痫之间相互作用，但有关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。由于高虚假预测率，现有的癫痫发作预测算法（SPA）不足以用于癫痫护理。最近的水疗评估表明，在睡眠，唤醒或一天中的某些时间，错误的癫痫发作预测更有可能。这些观察结果表明，（1）水疗中心将某些正常的警惕状态与真正的前后状态（如果存在）或（2）水疗中心衡量仅跟踪到正常警惕状态的过渡，这些状态更容易受到癫痫发作。为了研究从不同大脑状态的癫痫发作的机理，需要方法来准确跟踪SOV的连续生理测量。我们已经开发了SOV歧视方法，该方法利用了从头部安装的加速度计的加速度测量值，以显着改善仅依赖脑电图的方法（Sunderam等，J。Neurosci。方法，2007年）。我们建议通过（1）应用有监督的费舍尔歧视和无监督的贝叶斯歧视来实时跟踪SOV的方法，以建立Sov的地面真相标签；（2）构建状态空间模型（隐藏的马尔可夫模型和切换卡尔曼过滤器）以跟踪嘈杂的非机构SOV动力学。在当前项目中，我们将使用慢性脑电图和运动学测量值测试这些方法执行准确，实时的大脑状态跟踪的能力，并从颞叶癫痫的啮齿动物模型以及正常对照组中进行测量。该输出将用于量化不同警惕状态的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，并以癫痫发作的可能性很高调查行为状态。癫痫发作易感性行为指数的表述将有助于改善水疗的性能，并使用低频电场调制，作为州依赖性癫痫发作检测，预期和预防性控制的基础。这对于测试候选水疗中心是否真正区分了preseizure时期和正常行为状态也将很有用。公共卫生相关性：我们的长期目标是开发用于植入癫痫发作的控制设备，以及用于实时跟踪癫痫患者警惕状态的方法。某些州，特别是与睡眠和唤醒有关的状态，更有可能导致癫痫发作。癫痫敏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提高。