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

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.

Study Overview

• Status: Completed
• Conditions: Epilepsy
• Intervention / Treatment: Device: Nelli

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.

• Study Type: Observational
• Enrollment (Actual): 150

Contacts and Locations

Study Locations

• United States
  • Tennessee
    • Memphis, Tennessee, United States, 38163
      • The University of Tennessee Health Science Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 6 years to 21 years (Child, Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients aged 6-21 with history (or suspected history) of motor seizures that are undergoing video-EEG monitoring for routine clinical care.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity of a seizure detection system	To show that Nelli is able to correctly identify each category of seizures separately (Category I, II, and III) and all seizures categories combined with a sensitivity of at least 70%. Hypotheses will be tested sequentially (all seizures combined, Category I, then Category II, then Category III), each with a significance level of 2.5%, and will continue until the first hypothesis is not rejected. For each detected abnormal event, the probability is calculated and concluded as seizure/non- seizure using predefined threshold values, pre-trained seizure detection library, and probability of that event. The time-points are reported automatically into the Dashboard of Nelli. Statistical analyses will be performed to calculate true and false positive and negative detection rates.	During routine video-EEG monitoring, up to 14 days

Collaborators and Investigators

• Sponsor: Neuro Event Labs Inc.
• Investigators: Principal Investigator: James Wheless, MD, The University of Tennessee Health Science Center

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2022
• Primary Completion (Actual): December 31, 2025
• Study Completion (Actual): January 31, 2026

Study Registration Dates

• First Submitted: October 25, 2022
• First Submitted That Met QC Criteria: November 3, 2022
• First Posted (Actual): November 4, 2022

Study Record Updates

• Last Update Posted (Actual): February 13, 2026
• Last Update Submitted That Met QC Criteria: February 11, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Epilepsy
• Other Study ID Numbers: 21-08296-XP

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No