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.

项目总结