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

摘要 Eysz，Inc.正在开发一种算法和软件解决方案，以可靠且负担得起的方式检测癫痫发作 使用现有智能玻璃技术的可移动环境。在一项概念验证研究中，艾斯能够检测到 75%的失神发作持续时间超过10 S，仅使用眼球测量变量(例如，瞳孔大小、瞳孔 位置、偏心、眨眼频率)使用现成的眼球跟踪技术检测。Eysz寻求建立 通过开发和商业化高度敏感和特定的癫痫发作检测算法来实现这一发现 使用眼动数据作为输入，最终扩展到其他癫痫类型。这项技术具有 有可能改变癫痫患者癫痫发作的检测和治疗，癫痫是最常见的癫痫之一 世界各地的神经性疾病。及时治疗可以将额外癫痫发作的机会减少一半，使 早期发现和治疗至关重要。不幸的是，使用电流可能很难检测和诊断 技术，特别是在失神发作等症状很少的癫痫类型中。金子 检测和表征癫痫发作活动的标准是脑电(EEG)视频监测 以及随后由训练有素的临床医生进行的审查，但这并不能很好地转化为门诊设置。而当 已经尝试开发动态脑电，这些都有很大的缺点，包括贫穷的患者 可接受性差，检测能力差，持续依赖非同步审查。额外的非EEG- 基于运动检测的设备仅限于强直-阵挛发作，这是导致一小部分 所有癫痫发作活动。因此，迫切需要可靠地检测临床外的癫痫发作，以提供 医生提供必要的信息来指导治疗决策。为了满足这一需求，Eysz 开发一个数字健康平台，利用现有的眼睛跟踪技术来满足这一重大未满足的需求 市场缺口大，技术上可行，资本高效，稳健，创新。Eysz计划使用现有的 智能玻璃技术可输出必要的视力数据，以供我们的癫痫检测进行分析 算法。我们还将构建数据库、软件系统和用户界面，使结果数据能够 存储在云中，并由医生可视化/分析。在第一阶段SBIR中，Eysz将推进 癫痫发作检测算法的发展：1)在≥100机上获取眼球测量视频和脑电数据 来自多个患者的失神发作，以及2)使用ML和统计方法来优化算法 使用眼球跟踪数据识别失神发作，目标灵敏度为85%，特异度为90%。 从这项研究中吸取的经验教训将(通过不同的训练集)应用于其他类型的癫痫，例如 局灶性意识受损(以前称为复杂部分性)发作，是成人最常见的发作类型。 这项工作对实地至关重要，癫痫基金会和 在癫痫基金会第8届年度鲨鱼缸中获得评委和人民选择奖 竞争。

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.