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.

Completed

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Key information

Conditions

Epilepsy Brain Diseases Central Nervous System Diseases Nervous System Diseases

Age range

18 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Thomas Jefferson University

Philadelphia, Pennsylvania, 19107, United States

About this study

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%.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

Treatment and study plan

Nelli

Device

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.

Primary outcomes

Sensitivity of a seizure detection system

Time frame: During routine seizure monitoring in the hospital - up to one week

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)

Sponsors and collaborators

Lead sponsor

Neuro Event Labs Inc.

Industry

Registry information

Important dates

Study start: 2020
Primary completion: 2022
Study completion: 2022
First posted: Feb 4, 2021
Registry last updated: Nov 25, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed