# Machine learning approaches for improving EEG data utility in SUDEP research

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $251,591

## Abstract

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.

## Key facts

- **NIH application ID:** 10593406
- **Project number:** 3R01NS123928-02S1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Orrin Devinsky
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $251,591
- **Award type:** 3
- **Project period:** 2021-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593406

## Citation

> US National Institutes of Health, RePORTER application 10593406, Machine learning approaches for improving EEG data utility in SUDEP research (3R01NS123928-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10593406. Licensed CC0.

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