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（由适用提供）：对于大多数患有癫痫病的人，癫痫发作的事件相对占据一生的一小部分。尽管他们的生命只有0.01％的癫痫发作（通常每月只有几分钟），但癫痫患者每天服用抗癫痫药（AED），遭受与AED相关的副作用，并在下一次癫痫发作时，他们的生命在起草。癫痫发作的外观随机性与重大的心理后果有关。此外，尽管每天的AED大约有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病及其心理影响。低癫痫发作概率的周期不需要AED或至少较低剂量的AED，从而减少了AED暴露及其副作用。高癫痫发作概率的时期可能会对急性AED作出反应，并且患者可以改变其活动以避免受伤。患者将有权使用可靠的癫痫森林来管理其药物和生活活动。在这笔赠款中，我们调查了癫痫发作是可预测的事件，并且使用近期支持向量机器（SVM），数据分析模型和通用SVM的实用准确，临床相关的癫痫发作预测，并应用于焦点犬癫痫中连续的颅内EEG（IEEEG）。这是建立局灶性癫痫的新治疗范式的第一步，从而不断跟踪癫痫发作的可能性以进行患者警告和智能反应疗法。天然发生的局灶性犬癫痫是用于癫痫发作预测投资的绝佳模型，因为与局灶性人癫痫的临床和电论学相似。这项研究提供了一个独特的机会，可以在均匀条件下（相同的环境）在天然发生的犬癫痫中研究癫痫发作预测。重要的是，狗足够大，可以容纳设计用于人类使用的设备。推动该提案的假设是，局灶性癫痫发作不是随机事件，并且存在与癫痫发作的低概率相关的大脑状态，并且可以使用机器学习方法（SVM＆Comeumum-SVM）可靠地对这些状态进行可靠分类，以结合IEEG，行为状态跟踪和电力标志（ECG）心脏速率的特征。该提议的目的是 在无法进行良好的预测时，开发可靠的癫痫发作预测（并在可能的情况下），并改善理解（数据表征）。