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

NCT ID: NCT04738552

Last Updated: 2024-11-25

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 233 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-01-09
• Study Completion Date: 2022-11-27

Brief 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.

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%.

Conditions

Epilepsy

Study Design

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

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

-

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 99 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Neuro Event Labs Inc.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Michael Sperling, MD

Role: PRINCIPAL_INVESTIGATOR

Jefferson University

Locations

Thomas Jefferson University

Philadelphia, Pennsylvania, United States

Countries

United States

Other Identifiers

TJU-20D.009

Identifier Type: -

Identifier Source: org_study_id