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: 10372655
• 负责人: Rachel Kuperman
• 金额: $ 5.2万
• 依托单位: EYSZ, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372655
• 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.

抽象的 Eysz, Inc. 正在开发一种算法和软件解决方案，以可靠且经济的方式检测癫痫发作 在一项概念验证研究中，Eysz 能够使用现有的智能玻璃技术进行检测。 仅使用眼测量变量（例如瞳孔大小、瞳孔大小）的所有持续时间超过 10 秒的失神发作中，>75% 使用现成的眼球追踪技术检测到位置、偏心率、眨眼频率）。 基于这一发现，开发和商业化高度敏感和特定的癫痫检测算法 使用眼球运动数据作为输入，最终扩展到其他癫痫类型。 有可能改变癫痫患者癫痫发作的检测和治疗，癫痫是最常见的疾病之一 世界范围内的神经系统疾病的及时治疗可以将额外癫痫发作的几率减少一半，从而使癫痫发作的几率降低一半。 不幸的是，早期检测和治疗至关重要。 技术，特别是在几乎没有可观察到的症状（例如失神发作）的癫痫类型中。 检测和表征癫痫活动的标准是通过视频进行脑电图 (EEG) 监测 随后由训练有素的临床医生进行审查，但这并不能很好地应用于门诊环境。 已经尝试开发动态脑电图，但效果显着，包括贫困患者 可接受性、检测能力差以及继续依赖异步检查。 基于运动检测的设备仅限于强直阵挛性癫痫发作，这是导致一小部分癫痫发作的原因。 因此，迫切需要在诊所外可靠地检测癫痫发作，以提供治疗。 为了满足这一需求，Eysz 为医生提供了必要的信息来指导治疗决策。 开发一个数字健康平台，利用现有的眼球追踪技术来解决这一重大未满足的问题 Eysz 计划利用现有技术，满足市场需求，具有技术可行性、资本效率、稳健性和创新性。 智能玻璃技术可导出必要的眼科数据，以便我们的癫痫检测进行分析 我们还将构建数据库、软件系统和用户界面，使生成的数据能够 存储在云端并由医生可视化/分析 在第一阶段 SBIR 中，Eysz 将推进 通过以下方式开发癫痫检测算法：1) 获取 ≥100 的眼科视频和脑电图数据 来自多个患者的失神发作，以及 2) 使用机器学习和统计方法来优化算法 使用眼动追踪数据识别失神发作，目标敏感性为 85%，特异性为 90%。 从这项研究中学到的经验教训将应用于（使用不同的训练集）其他癫痫类型，例如 局灶性意识障碍（以前称为复杂部分性）癫痫发作，成人中最常见的癫痫发作类型。 这项工作对该领域至关重要，癫痫基金会和 在癫痫基金会第八届年度创智赢家大赛中获得评委奖和民众选择奖 竞赛。