Predicting seizures using physiological signals and machine learning

Reliable Seizure Prediction Using Physiological Signals and Machine Learning

NIH-funded research Mayo Clinic Rochester · NIH-11122331

This study is working on a new system that helps people with epilepsy by predicting when a seizure might happen, so they can adjust their treatment and activities to stay safe and feel more in control.

Quick facts

Grant typeR01 grant
Study typeNIH-funded research
Funding institutionMayo Clinic Rochester NIH-funded
Lab location1 site (Rochester, United States)
Project IDNIH-11122331 on NIH RePORTER

What this research studies

This research aims to improve the lives of individuals with epilepsy by developing a system that predicts seizures in real-time using physiological signals and machine learning techniques. By identifying periods of high seizure risk, the study seeks to enable adaptive therapies, such as adjusting medication dosages and implementing targeted brain stimulation to prevent seizures. This approach not only aims to reduce the frequency of seizures but also to alleviate the psychological burden associated with the unpredictability of seizure occurrences. Patients may also be able to modify their activities during high-risk periods to minimize injury.

Who could benefit from this research

Good fit: Ideal candidates for this research are individuals diagnosed with epilepsy who experience seizures and are currently on anti-seizure medications.

Not a fit: Patients who do not have epilepsy or those whose seizures are completely controlled by medication may not benefit from this research.

Why it matters

Potential benefit: If successful, this research could significantly enhance seizure management and improve the quality of life for patients with epilepsy.

How similar studies have performed: Previous research has shown promise in using machine learning for seizure prediction, indicating that this approach could lead to meaningful advancements in epilepsy treatment.

Where this research is happening

Rochester, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-09 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.