Reliable Seizure Prediction Using Physiological Signals and Machine Learning

使用生理信号和机器学习进行可靠的癫痫发作预测

• 批准号: 9238808
• 负责人: Gregory A Worrell
• 金额: $ 61.48万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-15 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9238808
• 关键词: Acute; Adverse effects; Algorithms; Anatomy; Animals; Antiepileptic Agents; Automobile Driving; Behavioral; Brain; Canis familiaris; Circadian Rhythms; Classification; Clinical; Data; Data Analytics; Device Designs; Dose; Drowsiness; Drug Exposure; Electrocardiogram; Electroencephalography; Employee Strikes; Environment; Epilepsy; Event; Focal Seizure; Goals; Grant; Heart Rate; High Frequency Oscillation; Hippocampus (Brain); Human; Individual; Injury; Investigation; Lead; Learning; Life; Machine Learning; Methodology; Modeling; Neocortex; Partial Epilepsies; Pathologic; Patients; Pattern; Pharmaceutical Preparations; Pharmacology; Physiological; Population; Probability; Psychological Impact; Scalp structure; Seizures; Signal Transduction; Sleep; Staging; Techniques; Thalamic structure; Time; Training; Validation; clinically relevant; empowered; heart rate variability; improved; novel; psychologic; public health relevance

 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.

 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，只占其生命的一小部分，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）。每天服用抗癫痫药物 (AED)，遭受与 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显着的心理相关。此外，尽管每天使用 AED，仍有大约 1/3 的患者继续出现癫痫发作，我们希望通过为患者提供实时癫痫发作预测来更有效地治疗癫痫发作及其心理影响。癫痫发作概率不需要 AED，或者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会对急性 AED 产生反应，并且患者可以改变其活动以避免受伤。他们的在这项资助中，我们研究了癫痫发作是可预测事件的假设，并利用支持向量机 (SVM)、数据分析模型和 Universum-SVM 的最新进展来追求准确的、临床相关的癫痫发作预测。应用于局灶性犬癫痫的连续颅内脑电图（iEEG），这是建立局灶性癫痫新治疗模式的第一步，可以持续跟踪癫痫发作的概率，以向患者发出警告并进行智能响应治疗。自然发生的犬局灶性癫痫是研究癫痫发作预测的绝佳模型，因为其临床和电生理学与人类局灶性癫痫相似，这项研究为研究统一条件（相同环境）下自然发生的犬癫痫的癫痫发作预测提供了独特的机会。重要的是，狗足够大，可以容纳专为人类使用而设计的设备，推动这一提议的假设是局灶性癫痫发作不是随机事件，并且存在与癫痫发生的低或高概率相关的大脑状态，并且这些状态可以是随机的。使用结合了 iEEG、行为状态跟踪和心电图 (ECG) 心率变异性的机器学习方法（SVM 和 Universum-SVM）进行可靠分类。 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时提高理解（数据特征）。