Algorithm for the Real-Time Detection of Absence Seizures from Oculometric Data

根据眼科数据实时检测失神发作的算法

• 批准号: 10421230
• 负责人: Rachel Kuperman
• 金额: $ 13.8万
• 依托单位: EYSZ, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10421230
• 关键词: Absence Epilepsy; Activities of Daily Living; Address; Adult; Advanced Development; Age; Algorithmic Software; Algorithms; Anticonvulsants; Award; Awareness; Blinking; Capital; Cessation of life; Childhood; Chronic; Clinic; Clinical Research; Clinical Trials; Complex; Data; Data Set; Databases; Decision Making; Detection; Development; Devices; Diagnosis; Early Diagnosis; Early treatment; Electrodes; Electroencephalogram; Epilepsy; Eye Movements; Focal Seizure; Foundations; Frequencies; Future; General Population; Glass; Gold; Impairment; Individual; Letters; Location; Machine Learning; Medical; Monitor; Morbidity - disease rate; Morphologic artifacts; Motion; Movement; Outpatients; Patients; Performance; Phase; Physicians; Pupil; Quality of life; Resolution; Risk; Seizures; Shark; Small Business Innovation Research Grant; Specificity; Statistical Data Interpretation; Statistical Methods; Symptoms; Technology; Testing; Therapeutic; Time; Tonic - clonic seizures; Training; Treatment Protocols; Work; algorithm development; base; commercialization; detection platform; digital health; disability; experience; high risk; improved; improved outcome; innovation; large datasets; machine learning method; mortality; mortality risk; nervous system disorder; premature; prospective; software systems; statistical and machine learning; visual tracking; wearable device

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.

摘要