Algorithm for the Real-Time Detection of Absence Seizures from Oculometric Data - Abstract
Eysz, Inc. is developing an algorithm and software solutions to reliably and affordably detect seizures in an
ambulatory setting using existing smart glass technologies. In a proof-of-concept study, Eysz was able to detect
>75% of all absence seizures longer than 10 s in duration using only oculometric variables (e.g., pupil size, pupil
location, eccentricity, blink frequency) detected using off-the-shelf eye-tracking technology. Eysz seeks to build
on this finding by developing and commercializing highly sensitive and specific seizure detection algorithms
using eye-movement data as input, with eventual expansion to additional seizure types. This technology has the
potential to transform the detection and treatment of seizures for those with epilepsy, one of the most common
neurological disorders worldwide. Timely treatment can reduce the chance of additional seizures by half, making
early detection and treatment critical. Unfortunately, detection and diagnosis can be difficult using current
technologies, especially in types of epilepsy with few observable symptoms such as absence seizures. The gold
standard for detecting and characterizing seizure activity is electroencephalogram (EEG) monitoring with video
and subsequent review by a trained clinician, but this does not translate well to the outpatient setting. While
attempts to develop ambulatory EEGs have been made, these have significant drawbacks, including poor patient
acceptability, poor detection capability, and continued reliance on asynchronous review. Additional non-EEG-
based motion detection devices are limited to tonic-clonic seizures, which are responsible for a small fraction of
all seizure activity. Thus, there is a critical need to reliably detect seizures outside of the clinic to provide
physicians with necessary information to guide therapeutic decision making. To address this need, Eysz is
developing a digital health platform that leverages existing eye tracking technology to meet this significant unmet
gap in the market and is technically feasible, capital-efficient, robust, and innovative. Eysz plans to use existing
smart glass technology to export the necessary oculometric data to be analyzed by our seizure detection
algorithm. We will also build out databases, software systems, and user interfaces enabling the resulting data to
be stored in the cloud and visualized/analyzed by physicians. In this Phase I SBIR, Eysz will advance the
development of the seizure detection algorithms by: 1) obtaining oculometric video and EEG data on ≥100
absence seizures from multiple patients, and 2) using ML and statistical methods to optimize an algorithm for
identifying absence seizures using eye-tracking data, with a target sensitivity of 85% and specificity of 90%.
Lessons learned from this study will be applied (with different training sets) to additional seizures types, such as
focal impaired awareness (formerly called complex partial) seizures, the most prevalent seizure type in adults.
This work is of critical importance to the field, as demonstrated by support from the Epilepsy Foundation and
receipt of both the judges' and people's choice awards in the Epilepsy Foundation's 8th Annual Shark Tank
Competition.
