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; 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，负担得起的检测 使用现有智能玻璃技术的门诊设置。在概念验证的研究中，Eysz能够检测到 仅使用眼镜变量（例如，学生大小，学生 位置，偏心率，眨眼频率）使用现成的眼睛追踪技术检测到。 Eyess试图建造 在这一发现中，通过开发和商业化高度敏感和特定的癫痫检测算法 使用眼动数据作为输入，最终扩展到其他癫痫发作类型。这项技术具有 改变癫痫患者的癫痫发作的检测和治疗的潜力，这是最常见的 全球神经疾病。及时的治疗可以减少额外癫痫发作的机会，从而使 早期检测和治疗至关重要。不幸的是，使用当前很难检测和诊断 技术，尤其是在癫痫的类型中，几乎没有可观察到的符号，例如缺席癫痫发作。黄金 检测和表征癫痫活性的标准是用视频监视的脑电图（EEG） 并随后由训练有素的临床医生进行审查，但这并不能很好地转化为门诊环境。尽管 已经进行了开发室外脑电图的尝试，这些试图具有明显的缺点，包括贫穷的患者 可接受性，检测能力不佳，并继续保留异步审查。其他非EEG- 基于运动检测设备仅限于强直性癫痫发作，这是一小部分 所有癫痫发作活动。这是迫切需要可靠的检测诊所以外的癫痫发作以提供 医师提供必要的信息来指导治疗决策。为了满足这一需求，Eysz是 开发一个数字健康平台，该平台利用现有的眼动追踪技术来满足这种重要的未满足 在市场上差距，在技术上是可行的，资本效率，健壮和创新的。 Eysz计划使用现有 智能玻璃技术以导出我们的癫痫发作检测来分析必要的眼镜测量数据 算法。我们还将构建数据库，软件系统和用户界面，使所得数据能够 存放在云中，并被医生可视化/分析。在这个阶段我的sbir，eysz将推进 发作检测算法的开发：1）获取≥100 多个患者的癫痫发作和2）使用ML和统计方法优化算法 使用眼睛跟踪数据鉴定缺席癫痫发作，目标灵敏度为85％，特异性为90％。 从这项研究中汲取的经验教训将被应用于其他癫痫发作类型，例如 局灶性障碍意识（以前称为复杂的部分）癫痫发作，是成年人中最普遍的癫痫发作类型。 这项工作对该领域至关重要，如癫痫基金会的支持所证明的那样 在癫痫基金会的第八届年度鲨鱼坦克中获得法官和人民选择奖 竞赛。