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

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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.