# Reliable Seizure Prediction Using Physiological Signals and Machine Learning

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2024 · $569,171

## Abstract

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.

## Key facts

- **NIH application ID:** 10864028
- **Project number:** 5R01NS092882-08
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Gregory A Worrell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $569,171
- **Award type:** 5
- **Project period:** 2022-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10864028

## Citation

> US National Institutes of Health, RePORTER application 10864028, Reliable Seizure Prediction Using Physiological Signals and Machine Learning (5R01NS092882-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10864028. Licensed CC0.

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