A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning - Nelli

NCT ID: NCT05606575

Last Updated: 2026-02-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 150 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-08-01
• Study Completion Date: 2026-01-31

Brief Summary

Nelli is a video-based non-EEG physiological seizure monitoring system. This study is a blinded comparison of Nelli's identified events to gold-standard video EEG review in at-rest pediatric subjects with suspected motor seizures.

Detailed Description

Automated analysis of video recordings to detect seizures, assisted by modern methods of machine learning, holds great promise to address this issue. Increased computational power has made it possible to implement complex image recognition tasks and machine learning in everyday use. Nelli® software is designed to use computer vision and machine learning-based algorithms to automatically detect seizure events. This study will provide evidence that Nelli software can identify seizure events and deliver objective data to clinicians for evaluation of seizure management.

This study is being conducted to validate the Nelli Software's ability to identify periods of audio

/video data that contain recordings of patients experiencing seizures (or seizure-like events) during periods of rest. The software's performance will be compared to the gold standard, expert review of video EEG data.

Nelli Software will review the audio and video data and independently identify events with positive motor manifestations. The outcomes of event identification will be compared between epileptologists and the Nelli Software. For each category of event captured the positive percent agreement will be calculated using the exact binomial method. The primary endpoint of this study is to demonstrate that Nelli is able to identify seizures that have a positive motor component with a sensitivity of >70% (lower 95% CI) and with a false discovery rate (FDR) comparable to similar devices on the market.

Conditions

Epilepsy

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Interventions

Nelli

Nelli is a non-EEG physiological signal-based seizure detection and quantification device that is indicated for use as an adjunct to seizure monitoring during periods of rest. The device utilizes automated analysis of audio and video (media) data collected via the personal recording unit (PRU) hardware accessory to identify epileptic and non-epileptic seizure events with a positive motor component.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Subject shall sign informed consent.
• Subject is between 6 and 21 years.
• Subjects shall be undergoing video-EEG monitoring for routine clinical purposes.
• Subjects shall have a suspected history of motor seizures.
• Subject shall be able to understand and sign written informed consent or have a legally authorized representative (LAR) who can do so, prior to the performance of any study assessments.

Exclusion Criteria

• None identified.

• Minimum Eligible Age: 6 Years
• Maximum Eligible Age: 21 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Neuro Event Labs Inc.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

James Wheless, MD

Role: PRINCIPAL_INVESTIGATOR

The University of Tennessee Health Science Center

Locations

The University of Tennessee Health Science Center

Memphis, Tennessee, United States

Countries

United States

Other Identifiers

21-08296-XP

Identifier Type: -

Identifier Source: org_study_id