Reliable Seizure Prediction Using Physiological Signals and Machine Learning

 描述（由申请人提供）：对于大多数癫痫患者来说，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%的时间发生癫痫发作（通常每月只有几分钟），但癫痫患者每天都服用抗癫痫药物（AED），遭受AED相关的副作用，并且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，尽管每天使用AED，仍有约1/3的患者继续癫痫发作。我们假设，通过为患者提供实时癫痫发作预测，可以更有效地治疗癫痫发作及其心理影响。癫痫发作概率低的时期不需要AED，或至少需要较低剂量的AED，从而减少AED暴露及其副作用。癫痫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损伤。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。在这项研究中，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Universum-SVM应用于连续颅内EEG（iEEG）在局灶性犬癫痫的最新进展，追求准确的，临床相关的癫痫发作预测。这是为局灶性癫痫建立新的治疗模式的第一步，从而持续跟踪癫痫发作发生的概率，以便对患者进行警告和智能响应治疗。自然发生的局灶性犬癫痫是一个很好的模型，研究癫痫发作预测，因为临床和电生理学相似的局灶性人类癫痫。这项研究提供了一个独特的机会，研究癫痫发作预测自然发生的犬癫痫在统一的条件下（相同的环境）。重要的是，狗足够大，可以容纳为人类设计的设备。驱动该提议的假设是局灶性癫痫发作不是随机事件，并且存在与癫痫发作发生的低概率或高概率相关联的大脑状态，并且这些状态可以使用机器学习方法（SVM & Universum-SVM）可靠地分类，所述机器学习方法（SVM & Universum-SVM）结合来自iEEG、行为状态跟踪和心电图（ECG）心率变异性的联合收割机特征。本提案的目的是 （在可能的情况下）进行可靠的缉获量预测，并在无法进行良好预测的情况下增进了解（数据定性）。

 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.