Reliable Seizure Prediction Using Physiological Signals and Machine Learning

 描述(由申请人提供)：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，占据了他们生活的一小部分。尽管癫痫患者一生中只有0.01%的时间患有癫痫(通常每月只有几分钟)，但他们每天都在服用抗癫痫药物(AED)，遭受与AED相关的副作用，一生都在担心下一次癫痫发作的时间。癫痫发作的明显随机性与严重的心理后果有关。此外，尽管每天都有AED，但大约三分之一的患者仍有癫痫发作。我们假设，通过为患者提供实时的癫痫发作预测，可以更有效地治疗癫痫，包括癫痫发作及其对心理的影响。发作概率低的时期不需要AEDs，或者至少需要较低剂量的AEDs，从而减少AEDs的暴露及其副作用。发作概率高的时期可能对急性AED有反应，患者可以改变他们的活动以避免受伤。患者将有权使用可靠的癫痫发作预测来管理他们的药物和生活活动。在这项授权中，我们研究了癫痫发作是可预测事件的假设，并使用支持向量机(SVM)、数据分析模型和优生支持向量机的最新进展来追求准确的、临床相关的癫痫发作预测，并将其应用于局灶性犬癫痫的连续颅内脑电(IEEG)。这是建立局灶性癫痫新治疗范例的第一步，根据该范例，持续跟踪癫痫发作发生的可能性，以进行患者警告和智能反应治疗。自然发生的犬局灶性癫痫是研究癫痫发作预测的一个很好的模型，因为它与人类局灶性癫痫的临床和电生理相似。这项研究提供了一个独特的机会来研究在相同条件下(相同环境)自然发生的犬癫痫发作预测。重要的是，狗足够大，可以容纳为人类设计的设备。支持这一建议的假设是，局灶性癫痫发作不是随机事件，存在与癫痫发作发生的低或高概率相关的大脑状态，并且这些状态可以使用结合了iEEG、行为状态跟踪和心电图心率变异性的特征的机器学习方法(支持向量机和优生和支持向量机)可靠地分类。这项提议的目标是 制定可靠的癫痫发作预测(如果可能)，并在无法进行良好预测的情况下改进理解(数据特征)。

 DESCRIPTION (provided by applicant): For most individuals living with epilepsy, seizures are relatively infrequent events occupying a small fraction of their life. Despite spending as little a 0.01% of their lives having seizures (typically only minutes per month), people with epilepsy take anti-epileptic drugs (AED) daily, suffer AED 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 AED 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. Periods of low seizure probability would not require AEDs, or at least lower doses of AEDs, thus reducing AED exposure and their side effects. Periods of high seizure probability may respond to acute AED and patients could alter their activities to avoid injury. Patients would be empowered to manage their medications and life activities using reliable seizure forecasts. In this grant we investigate the hypothesis that seizures are predictable events, and pursue accurate, clinically relevant seizure forecasting using recent advances in support vector machines (SVM), data-analytic models, and Universum-SVM applied to continuous intracranial EEG (iEEG) in focal canine epilepsy. This is an initial step in establishin a new treatment paradigm for focal epilepsy, whereby the probability of seizure occurrence is continuously tracked for patient warning and intelligent responsive therapies. Naturally occurring focal canine epilepsy is an excellent model for investigation of seizure forecasting because of the clinical and electrophsyiological similarity to focal human epilepsy. This study provides a unique opportunity to study seizure forecasting in naturally occurring canine epilepsy under uniform conditions (the same environment). Importantly, dogs are large enough to accommodate devices designed for human use. The hypotheses driving this proposal are that focal seizures are not random events and there are brain states associated with low or high probability of seizure occurrence, and that these states can be reliably classified using machine learning approaches (SVM & Universum-SVM) that combine features from iEEG, behavioral state tracking, and electrocardiogram (ECG) heart rate variability. The goal of this proposal is to develop reliable seizure forecasting (when possible) and improved understanding (data characterization) when good forecasting is not possible.