A Mobile Health Application to Detect Absence Seizures using Hyperventilation and Eye-Movement Recordings

一款使用过度换气和眼动记录检测失神癫痫发作的移动健康应用程序

• 批准号: 10696649
• 负责人: Rachel Kuperman
• 金额: $ 49.99万
• 依托单位: EYSZ, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10696649
• 关键词: Absence Epilepsy; Accidental Injury; Adoption; Affect; Agreement; Algorithmic Analysis; Algorithms; Anticonvulsants; Antiepileptic Agents; Appointment; Area; Awareness; Biometry; Caregivers; Cellular Phone; Child; Childhood; Clinic; Clinical; Clinical Research; Computer software; Data; Data Collection; Data Reporting; Detection; Development; Devices; Diagnosis; Diagnostic; Diagnostic Procedure; Effectiveness; Electroencephalography; Elements; Epilepsy; Evaluation; Exhalation; Eye Movements; Eyeglasses; Face; Foundations; Freedom; Frequencies; Future; Glass; Home; Home environment; Hour; Hyperventilation; Impairment; Improve Access; Interview; Label; Learning; Length; Low income; Marketing; Measures; Methods; Mobile Health Application; Monitor; Multi-site clinical study; Outcome Measure; Patient Self-Report; Patients; Performance; Persons; Phase; Population; Predisposition; Reading; Reporting; Risk; Rural; Safety; Seizures; Sensitivity and Specificity; Surveys; Syndrome; System; Telemedicine; Testing; Therapeutic Effect; Time; Training; Treatment Efficacy; Validation; Valproic Acid; Video Recording; Wait Time; Work; accurate diagnosis; childhood epilepsy; cohort; detection sensitivity; diagnosis standard; effectiveness evaluation; experience; improved outcome; mHealth; machine learning algorithm; machine learning method; novel therapeutics; patient population; primary care provider; remote diagnosis; remote monitoring; smartphone application; social stigma; software development; success; tool; treatment response; usability; visual tracking

Abstract Eysz, Inc. is developing a mobile health (mHealth) application and algorithms for diagnosing and monitoring absence epilepsy remotely. Accurate diagnosis and monitoring of seizures and therapeutic effects are critical elements of effective epilepsy treatment. Unfortunately, absence seizures are notoriously difficult to identify, leading to diagnostic delay and difficulty monitoring treatments. The gold standard for diagnosing absence seizures is video EEG (VEEG), but this method is expensive, limited to clinical settings, and can be hard to access. The gold standard for monitoring absence epilepsy is patient self-reported data, which studies have shown to be more than 50% inaccurate. Other strategies for remote monitoring, such as ambulatory EEG, lack the sensitivity and specificity of VEEG, and can add to the stigma people with epilepsy experience. There have been no new therapy approvals for absence epilepsy since the 1990s, in part due to the difficulty of measuring outcomes. Thus, there is a critical need for a remote diagnostic/monitoring tool for absence seizures. Eysz therefore plans to develop an mHealth app that uses (1) voluntary guided hyperventilation (HV), with (2) eye movement and facial biometric data to monitor seizure susceptibility and treatment responses in people with absence seizures. Voluntary HV triggers seizures in >90% of people with absence epilepsy and is a standard clinical tool to assist in diagnosing and monitoring absence epilepsy. HV has also been shown to be safe and effective when performed on a daily basis to activate seizures and thereby shorten VEEG monitoring sessions. Thus, HV offers a promising tool for use in the context of at-home monitoring of seizure activity. Eysz is developing software and algorithms for detecting seizures using eye movement data, starting with absence seizures. Eysz proposes to extend the use of video-based eye-tracking (and facial biometric tracking) to a smartphone-based application that includes software-guided HV. This Phase I proposal focuses on initial testing of our smartphone-based tool for guided HV and video data collection. The Specific Aims of this project are: 1) Collect eye-movement and facial biometric data from subjects undergoing HV concurrently with VEEG; 2) Evaluate the potential for a new “gold standard” metric for algorithm validation to enable mHealth development in the home environment; and 3) Develop machine learning (ML) algorithms that detect seizures from eye tracking and facial biometrics data. Eysz aims to demonstrate >75% sensitivity for detection of seizures >7 s in duration, providing a strong foundation for future evaluation of at-home use of the app and algorithm accuracy in a larger cohort of patients.

抽象的 Eysz，Inc。正在开发用于诊断和监视的移动健康（MHealth）应用程序和算法 遥不可及的癫痫病。准确的诊断和监测癫痫发作和治疗作用至关重要 有效癫痫治疗的要素。不幸的是，众所周知，缺席癫痫发作很难识别， 导致诊断延迟和困难监测治疗。诊断吸收的金标准 癫痫发作是视频脑电图（VEEG），但是这种方法昂贵，仅限于临床环境，很难很难 使用权。监测缺勤癫痫的黄金标准是患者自我报告的数据，研究具有 显示出超过50％的不准确。远程监控的其他策略，例如门诊脑电图，缺乏 Veeg的敏感性和特异性，可以增加具有癫痫经验的污名人士。那里有 自1990年代以来，我们没有新的疗法批准，部分原因是难以测量 结果。这是对吸收癫痫发作的远程诊断/监测工具的迫切需求。 Eysz 因此，计划开发使用（1）使用（1）使用（2）眼 运动和面部生物识别数据以监测患有患者的癫痫敏感性和治疗反应 缺席癫痫发作。 > 90％的缺席癫痫患者的自愿性HV触发癫痫发作是一种标准 帮助诊断和监测吸收癫痫的临床工具。 HV也已被证明是安全的 每天执行以激活癫痫发作，从而缩短VEEG监测会话时有效。 这是HV提供了一种有前途的工具，可用于在家监视癫痫发作活动的上下文中。 Eysz是 开发用于使用眼动数据检测癫痫发作的软件和算法，从缺席开始 癫痫发作。 Eyesz提议将基于视频的眼睛跟踪（和面部生物识别跟踪）扩展到 基于智能手机的应用程序，其中包括软件引导的HV。该阶段我的建议着重于初始测试 我们的基于智能手机的工具用于指导HV和视频数据收集。该项目的具体目的是：1） 从与VEEG同时接受HV的受试者中收集眼动和面部生物识别数据； 2） 评估用于算法验证的新“黄金标准”度量的潜力，以实现MHealth开发 在家庭环境中； 3）开发从眼睛检测癫痫发作的机器学习（ML）算法 跟踪和面部生物识别数据。 Eysz的目的是证明> 75％的敏感性，以检测癫痫发作> 7 s 持续时间，为将来对应用程序的使用和算法准确性评估提供了坚实的基础 在较大的患者中。