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: 10518240
• 负责人: Gregory A Worrell
• 金额: $ 56.46万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518240
• 关键词: Automobile Driving; Bilateral; Biological Markers; Body Temperature; Brain; Canis familiaris; Chronic; Clinical; Data Analytics; Development; Device Designs; Devices; Dose; Drug Side Effects; Effectiveness; Electric Stimulation; Electrical Stimulation of the Brain; Electrocardiogram; Electroencephalogram; Electroencephalography; Electrophysiology (science); Epilepsy; Event; Evoked Potentials; Focal Seizure; Goals; Grant; Hippocampus (Brain); Human; Immune Sera; Immunologic Factors; Immunologic Markers; Immunologics; Implant; Implantable Injection/Infusion Ports; Incidence; Individual; Injury; Intelligence; Investigation; Life; Machine Learning; Modeling; Neural Network Simulation; Partial Epilepsies; Patients; Performance; Persons; Pharmaceutical Preparations; Pharmacology; Physiological; Probability; Psychological Impact; Research; Risk; Risk Reduction; Sampling; Seizures; Series; Signal Transduction; Stream; Temperature; Temperature Sense; Therapy trial; Time; Work; circadian; convolutional neural network; deep learning; electric impedance; implantable device; improved; learning strategy; machine learning method; machine learning model; multimodal data; multimodality; prevent; prospective; psychologic; response; side effect; support vector machine; targeted treatment

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.

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，占癫痫发作的一小部分。 他们的生活.尽管他们一生中只有0.01%的时间癫痫发作（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ASD相关的副作用， 担心下一次癫痫发作何时发生。癫痫发作的明显随机性与显著的 心理后果。此外，尽管每天ASD，约1/3的患者继续有 癫痫发作。我们假设癫痫可以更有效地治疗，无论是癫痫发作和他们的 通过为患者提供实时癫痫发作预测， 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， 针对高癫痫发作概率时期的治疗。癫痫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。我们建议，高癫痫发作概率的国家将作出反应， 自适应脑电刺激（aEBS）。此外，患者可以改变他们的活动期间， 高癫痫发作概率，以减少损伤并管理他们的ASD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 发作前状态期间的EBS治疗2.）癫痫发作不是随机事件， 低和高癫痫发作概率可以使用机器学习方法可靠地分类， 生理信号，并用于自适应地改变EBS参数。3.）第三章此外，我们建议预测 可以使用被动iEEG记录以外的多模态功能（包括主动大脑探测）进行改进 电刺激（阻抗和诱发电位），核心温度，ECG和血清免疫学 标记。目标：开发可靠的癫痫发作预测（>90%的灵敏度），假阳性很少（<1%的时间）。 警告），并使用aEBS证明癫痫发作风险的调节和局灶性癫痫发作的减少。