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A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning (USF)

NCT06705439 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 33

Last updated 2026-02-13

No results posted yet for this study

Summary

Increased computational power has made it possible to implement complex image recognition tasks and machine learning to be implemented in every day usage. The computer vision and machine learning based solution used in this project (Nelli) is an automatic seizure detection and reporting method that has a CE mark for this specific use.

The present study will provide data to expand the utility and detection capability of NELLI and enhance the accuracy and clinical utility of automated computer vision and machine learning based seizure detection.

Conditions

Interventions

DEVICE

Nelli

Nelli detects and registers activity that is indicative of seizure events. Nelli captures, stores, and processes video and audio recordings from each patient. Biomarker data is collected during periods of rest for the length of an examination period, which may span several days or months (when used inside and outside of a hospital setting, respectively), as prescribed by a treating physician.

Sponsors & Collaborators

  • Neuro Event Labs Inc.

    lead INDUSTRY

Principal Investigators

  • Selim Benbadis, MD · University of South Florida

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-15
Primary Completion
2025-12-31
Completion
2026-01-31
FDA Device
Yes

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06705439 on ClinicalTrials.gov