Ignite Creation Date:
2024-05-06 @ 7:41 PM
Last Modification Date:
2024-10-26 @ 3:11 PM
Study NCT ID:
NCT06093490
Status:
RECRUITING
Last Update Posted:
2024-02-20
First Post:
2023-10-16
Brief Title:
Detecting Absence Seizures Using Hyperventilation and Eye Movement Recordings
Sponsor:
Eysz Inc
Organization:
Eysz Inc
Study Overview
Official Title:
A Mobile Health Application to Detect Absence Seizures Using Hyperventilation and Eye-Movement Recordings
Status:
RECRUITING
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
DASHER
Brief Summary:
This study is being done to find out if a smartphone app can identify absence seizures Children who have a history of absence seizures as well as children without any seizure history will be testing out the app If participating the child will be guided through hyperventilation an activity that asks the child to take quick deep breaths The app will record video of the childs face and sounds they make during hyperventilation Hyperventilation is a safe and established technique frequently used during EEG electroencephalogram to encourage seizure occurrence The App will be used during a regularly scheduled EEG
Detailed Description:
This observational study focuses on validating the use of the Eysz mHealth App - a smartphone-based tool for guided Hyperventilation HV and data collection - to aid clinicians in identifying absence seizures in people at risk for childhood absence epilepsy CAE The Eysz mHealth app will guide users through HV via interactive graphics while capturing audio and video data using smartphone sensors eg camera microphone from which eye movements facial biometrics expressions number and length of exhales will be extracted
The goal of this observational study is to determine an epileptologists performance in identifying HV-induced absence seizures using video data collected from a smartphone when compared to the gold standard interpretation of the EEG The exploratory goal is to develop machine learning based algorithms to identify HV-induced seizures
The goal of this observational study is to determine an epileptologists performance in identifying HV-induced absence seizures using video data collected from a smartphone when compared to the gold standard interpretation of the EEG The exploratory goal is to develop machine learning based algorithms to identify HV-induced seizures
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
True
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 1R43NS129363 | NIH | None | httpsreporternihgovquickSearch1R43NS129363 |