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 父R 01项目将检验癫痫意外猝死（SUDEP）病例 表现出不同的临床、电临床和成像特征，可以识别和验证（目标1），然后 将其纳入个体化贝叶斯风险预测模型（目标2）。该研究将比较SUDEP病例 与年龄/性别匹配的癫痫患者的生活，以确定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， 在常规临床访视中容易获得。潜在的生物标志物包括发作后全身性EEG抑制， 发作间期ECG心率变异性，结构MRI显示边缘系统和脑干区域体积减小 扫描。为了利用最先进的计算工具进行生物标志物发现，母体R 01的Aim 3采用了 人工智能（AI）和机器学习（ML）技术，从发作间期EEG中发现新的生物标志物 数据 拟议的补充项目与母公司R 01的目标3密切一致，并建立在基础上 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 具有互补的专业知识，能够为EEG数据AI/ML开发创新的分析工具- 准备就绪。在目标1中，我们将开发ML模型来增强数据解释。在目标2中，我们将使用数据 增强技术，以提高标记的EEG数据的一致性，从SUDEP的情况下， 癫痫患者控制。总的来说，这一行政补充建议将进一步丰富研究目标 在我们的母基金，并促进研究的严谨性，透明度和可重复性。实现这些目标将 最大化数据效用，提高癫痫研究中的AI/ML准备程度。