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

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.

Study Overview

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

Detailed Description

This is a prospective, blind comparison to the clinical gold standard for seizure characterization. This study is intended to compare the Nelli Software's ability to identify seizure events to vEEG review in adults with suspected nighttime seizures. Simultaneously, Nelli will continuously record audio and video while video-electroencephalography (vEEG) is recorded per typical standard of care. Events with positive motor manifestations will be independently identified, following standard clinical practice, by three epileptologists using clinical vEEG 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%.

• Study Type: Observational
• Enrollment (Actual): 167

Contacts and Locations

Study Locations

• United States
  • Pennsylvania
    • Philadelphia, Pennsylvania, United States, 19107
      • Thomas Jefferson University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 99 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients with suspected motor seizures that are undergoing video-EEG monitoring for routine clinical care.

Description

Inclusion Criteria:

• All patients undergoing video-EEG monitoring for clinical purposes who are suspected of having seizures.

Exclusion Criteria:

-

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	The primary outcome measure will be the sensitivity of the Nelli system to detect seizrues with a positive motor component in comparison to independent Neurologist review of vEEG collected in an epilepsy monitoring unit. This is a blinded comparison to the clinical gold standard (vEEG)	During routine seizure monitoring in the hospital - up to one week

Collaborators and Investigators

• Sponsor: Neuro Event Labs Inc.

Study record dates

Study Major Dates

• Study Start (Actual): January 9, 2020
• Primary Completion (Actual): October 31, 2021
• Study Completion (Actual): October 31, 2021

Study Registration Dates

• First Submitted: January 29, 2021
• First Submitted That Met QC Criteria: February 3, 2021
• First Posted (Actual): February 4, 2021

Study Record Updates

• Last Update Posted (Actual): May 31, 2022
• Last Update Submitted That Met QC Criteria: May 27, 2022
• Last Verified: May 1, 2022

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: TJU-20D.009

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: No