# Focal seizure monitoring with a consumer wearable: algorithmic development and validation

> **NIH NIH R41** · EPIWATCH, INC. · 2024 · $275,547

## Abstract

PROJECT SUMMARY
Uncontrolled epilepsy is a major public health burden, affecting over 1.5 million patients in the US, resulting in
total direct costs of up to $50,000 per year for individual patients. Generalized tonic-clonic seizures (GTCS) have
been the focus of wearable-based seizure monitoring research for the past decade, while little work has been
done in the detection of other seizure types. Focal seizures (FS) in particular, are the most common seizure
type, affecting over 60% of people with active epilepsy. Accurate FS detection can promote safety, providing
real-time alerting to caregivers, and can support more accurate seizure tracking, bypassing the need for manual
diaries and providing important data for physicians to better manage epilepsy. Our team has developed a
software application on a popular consumer wearable device, to record data from accelerometer (ACC) and
photoplethysmography (PPG) biosensors, with the goal of providing an easy, non-stigmatizing method for FS
monitoring.
We propose a new methodology of detection that leverages the unique seizure characteristics of focal seizures
to substantially reduce the rate of false alarms, which can result in poor compliance with monitoring. Our
approach is based on the scientific premise that while focal seizures can vary significantly across individuals,
they are usually far less variable within individuals, owing to their propensity for onset and propagation in the
same symptomatogenic zones. We propose an adaptive methodology that can accurately classify FSs for
specific individuals over time. We are uniquely positioned to complete this goal, as we can leverage the
thousands of hours of data we have collected from previous trials, and our team has extensive prior experience
in training and testing seizure detection algorithms. In this proposal, we plan to develop our algorithm through
the following aims: (1) Developing a patient-independent focal seizure classification methodology leveraging
data we have obtained from previous IRB approved research. (2) Enhancing our algorithm by creating a patient-
dependent classification methodology. This aim, in particular, will allow the proposed algorithm to significantly
reduce false alarm rates (FARs). (3) Prospectively validating the proposed algorithm in an observational study.
We expect the final detector to significantly improve FAR without sacrificing sensitivity. If successful, we will
submit a Phase II proposal focused on further validation, expansion to include ambulatory patients, and
commercialization.
Our overall goal is to use our clinical and technical expertise to significantly improve the lives of people with
epilepsy. We believe that with this algorithm technology on the EpiWatch digital health platform, we can help
ease the physical, mental, and financial burdens of uncontrolled epilepsy, thus improving quality-of-life for people
with epilepsy.

## Key facts

- **NIH application ID:** 10604654
- **Project number:** 1R41NS130958-01
- **Recipient organization:** EPIWATCH, INC.
- **Principal Investigator:** NATHAN E CRONE
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $275,547
- **Award type:** 1
- **Project period:** 2024-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10604654

## Citation

> US National Institutes of Health, RePORTER application 10604654, Focal seizure monitoring with a consumer wearable: algorithmic development and validation (1R41NS130958-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10604654. Licensed CC0.

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