Machine learning approaches for improving EEG data utility in SUDEP research

用于提高 SUDEP 研究中脑电图数据效用的机器学习方法

• 批准号: 10593406
• 负责人: Orrin Devinsky
• 金额: $ 25.16万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593406
• 关键词: Address; Administrative Supplement; Adopted; Age; Algorithms; Applications Grants; Area Under Curve; Artificial Intelligence; Benchmarking; Big Data; Biological Markers; Brain Stem; Cessation of life; Clinical; Collaborations; Complex; Counseling; Data; Data Analyses; Data Science; Data Set; Development; Electrocardiogram; Electroencephalography; Ensure; Epilepsy; Exhibits; Feedback; Generations; Goals; Grant; Human; Image; Individual; Intervention; Label; Learning; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Medicine; Methods; Modeling; Morphologic artifacts; Natural regeneration; Neurologic; Neurology; Parents; Patients; Performance; Persons; Prevention strategy; Process; Psychiatry; Publishing; Readiness; Records; Reproducibility; Research; Retrospective cohort study; Risk; Risk Factors; Sample Size; Sampling; System; Techniques; Testing; United States National Institutes of Health; Validation; Visit; analytical tool; base; biomarker discovery; candidate marker; case control; computerized tools; data cleaning; data format; data quality; data standards; deep learning; deep learning model; distributed data; experience; frontier; heart rate variability; high risk; improved; innovation; interest; learning strategy; machine learning method; machine learning model; medical schools; mortality; multimodal data; multimodality; novel marker; open data; parent grant; parent project; potential biomarker; predictive modeling; repository; research study; response; risk prediction; risk prediction model; screening; sex; success; sudden unexpected death in epilepsy; translational impact

Project Summary The parent R01 project will test the hypothesis that Sudden Unexpected Death in Epilepsy (SUDEP) cases exhibit different clinical, electroclinical and imaging features that can be identified and validated (Aim 1) and then incorporated into an individualized Bayesian risk prediction model (Aim 2). The study will compare SUDEP cases with age/sex-matched living epilepsy patients to identify clinical features and biomarkers, focusing on electroencephalography (EEG), electrocardiogram (ECG), and magnetic resonance imaging (MRI) data that are easily obtained during routine clinical visits. Potential biomarkers include postictal generalized EEG suppression, interictal ECG heart-rate variability, and decreased volume in limbic and brainstem regions on structural MRI scans. To leverage state-of-the-art computational tools for biomarker discovery, the parent R01’s Aim 3 employs artificial intelligence (AI) and machine learning (ML) techniques to uncover novel biomarkers from interictal EEG data. The proposed supplemental project is closely aligned with the parent R01’s Aim 3 and builds on the base of augmented datasets and new AI/ML techniques. Our research team consists of SUDEP and AI/ML experts with complementary expertise who are uniquely qualified to develop innovative analytic tools for EEG data AI/ML- readiness. In Aim 1, we will develop ML models to enhance data interpretation. In Aim 2, we will employ data augmentation techniques to improve the consistency of labeled EEG data from both SUDEP cases and living epilepsy patient controls. Overall, this administrative supplemental proposal will further enrich the research aims in our parent grant, and promote research rigor, transparency and reproducibility. Accomplishing these aims will maximize the data utility and improve AI/ML-readiness in epilepsy research.

项目摘要 家长R01项目将检验以下假设：癫痫病（SUDEP）病例突然出现意外死亡 具有可以识别和验证的不同临床，电细胞和成像特征（AIM 1），然后 纳入个性化的贝叶斯风险预测模型（AIM 2）。该研究将比较SUDEP案例 随着年龄/性别匹配的生活癫痫患者，以识别临床特征和生物标志物，重点是 脑电图（EEG），心电图（ECG）和磁共振成像（MRI）数据是 在常规临床就诊期间很容易获得。潜在的生物标志物包括邮政通用脑电图抑制， 结构性MRI的边缘和脑干区域的间歇性心脏率变异性以及提高的体积 扫描。为了利用生物标志物发现的最新计算工具，父级R01的AIM 3员工 人工智能（AI）和机器学习（ML）技术，可从间歇性脑电图中发现新颖的生物标志物 数据。 拟议的补充项目与父母R01的AIM 3紧密一致，并在基础上建立 增强数据集和新的AI/ML技术。我们的研究团队由SUDEP和AI/ML专家组成 具有完善的专业知识，他们具有独特的资格来开发用于脑电图数据/ML-的创新分析工具 准备。在AIM 1中，我们将开发ML模型以增强数据解释。在AIM 2中，我们将采用数据 增强技术以提高SUDEP案例和生活的标记脑电图数据的一致性 癫痫患者对照。总体而言，这项行政补充提议将进一步丰富研究目的 在我们的父母赠款中，并促进了严格的研究，透明度和可重复性。完成这些目标将 最大化数据实用性并改善癫痫研究中的AI/ML准备度。