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测的移动健康 (mHealth) 应用程序和算法 远程癫痫的准确诊断和监测以及治疗效果至关重要。 不幸的是，失神发作很难识别， 导致诊断延迟和难以监测治疗。 诊断缺勤的黄金标准。 癫痫发作的方法是视频脑电图（VEEG），但这种方法价格昂贵，仅限于临床环境，并且可能很难 监测失神癫痫的黄金标准是患者自我报告的数据，研究表明这些数据。 缺乏其他远程监测策略（例如动态脑电图），其准确性超过 50%。 VEEG 的敏感性和特异性，可能会增加有癫痫经历的人的耻辱感。 自 20 世纪 90 年代以来，没有新的治疗失神性癫痫的疗法获批，部分原因是难以测量 因此，迫切需要一种用于失神发作的远程诊断/监测工具。 因此计划开发一款移动健康应用程序，该应用程序使用 (1) 自愿引导过度通气 (HV)，以及 (2) 眼睛 运动和面部生物识别数据，用于监测癫痫患者的癫痫易感性和治疗反应 自愿性 HV 会导致 90% 以上的失神性癫痫患者出现癫痫发作，并且是一种标准情况。 协助诊断和监测失神性癫痫的临床工具也已被证明是安全且有效的。 每天执行可有效激活癫痫发作，从而缩短 VEEG 监测时间。 因此，HV 提供了一种很有前景的工具，可用于 Eysz 的家庭癫痫活动监测。 开发软件和算法，使用眼动数据检测癫痫发作，从失神开始 Eysz 提议将基于视频的眼球追踪（和面部生物识别追踪）的使用扩展到 基于智能手机的应用程序，包括软件引导的高压。第一阶段提案的重点是初始测试。 我们基于智能手机的引导高压和视频数据收集工具的具体目标是：1) 收集同时接受 HV 和 VEEG 的受试者的眼球运动和面部生物特征数据 2) 评估算法验证的新“黄金标准”指标的潜力，以促进移动医疗的发展 在家庭环境中；3) 开发检测眼睛癫痫发作的机器学习 (ML) 算法 Eysz 的目标是证明在 7 秒内检测癫痫发作的灵敏度 >75%。 持续时间，为未来评估应用程序的家庭使用情况和算法准确性提供坚实的基础 在更大的患者群体中。