Reliable Seizure Prediction Using Physiological Signals and Machine Learning

对于大多数患有癫痫的人来说，癫痫发作是相对罕见的事件，只占 他们的生活。尽管他们一生中只有0.01%的时间患有癫痫(通常每月只有几分钟)， 癫痫患者每天服用抗癫痫药物(ASD)，遭受与ASD相关的副作用，并花费一生 害怕下一次癫痫发作什么时候会发作。癫痫发作的明显随机性与显著的 心理上的后果。此外，尽管每天都有ASD，但大约三分之一的患者继续患有 癫痫发作。我们假设癫痫可以更有效地治疗，无论是癫痫发作还是癫痫发作 心理影响，通过为患者提供实时的癫痫发作预测。 有强有力的证据表明，局灶性癫痫与可变的癫痫发作风险有关，这可能使适应性 针对癫痫发作概率较高的时期进行治疗。发作概率较低的时期可能需要较低的 ASD剂量，减少暴露和副作用。我们提出，高俘获概率状态将对 适应性脑电刺激(AEBS)。此外，患者可能会改变他们的活动期间 高癫痫发作概率，以减少伤害，并管理他们的自闭症和活动。 推动这一提议的假设是1。癫痫发作可以通过有针对性的方法预防(减少发病率) 发作前状态下的EBS治疗2。)癫痫发作不是随机事件，大脑状态与 使用机器学习方法可以可靠地对低发作概率和高发作概率进行分类 生理信号，用于自适应地改变EBS参数。3.)此外，我们还提出了预测 可以使用被动iEEG记录之外的多模式功能来改进，包括主动大脑探测 电刺激(阻抗和诱发电位)、核心温度、心电图和血清免疫学 记号笔。目标：开发可靠的癫痫发作预测(&gt；90%灵敏度)，错误阳性很少(&lt；1%的时间在 警告)，并演示使用AEBS调节癫痫风险和减少局灶性癫痫发作。

For most individuals living with epilepsy, seizures are relatively infrequent events occupying a small fraction of their life. Despite spending as little as 0.01% of their lives having seizures (typically only minutes per month), people with epilepsy take anti-seizure drugs (ASD) daily, suffer ASD related side effects, and spend their lives dreading when the next seizure will strike. The apparent randomness of seizures is associated with significant psychological consequences. In addition, despite daily ASD, approximately 1/3 of patients continue to have seizures. We hypothesize that epilepsy can be more effectively treated, both the seizures and their psychological impact, by providing patients with real-time seizure forecasting. There is strong evidence that focal epilepsy is associated with a variable seizure risk that may enable adaptive therapy targeting periods of high seizure probability. Periods of low seizure probability could require lower ASD doses, reducing exposure and side effects. We propose that high seizure probability states will respond to adaptive electrical brain stimulation (aEBS). In addition, patients could alter their activities during periods of high seizure probability to reduce injury and manage their ASD and activities. The hypotheses driving this proposal are that 1.) seizures can be prevented (reduced incidence) by targeted EBS therapy during the pre-ictal state 2.) seizures are not random events, and that brain states associated with low and high seizure probability can be reliably classified using machine learning methods applied to physiologic signals and used to adaptively change EBS parameters. 3.) Furthermore, we propose forecasting can be improved using multi-modal features beyond passive iEEG recordings, including active brain probing with electrical stimulation (impedance & evoked potentials), core temperature, ECG and serum immunological markers. Goal: Develop reliable seizure forecasting (>90% sensitivity) with few false positives (<1% time in warning) and demonstrate modulation of seizure risk and reduction of focal seizures using aEBS.