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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

基本信息

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
项目类别：
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

项目摘要

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.

摘要