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

Epilepsy Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04738552
• Other study ID #: TJU-20D.009
• Status: Completed
• Start date: January 9, 2020
• Est. completion date: October 31, 2021

Study information

• Verified date: May 2022
• Source: Neuro Event Labs Inc.
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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%.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 167
• Est. completion date: October 31, 2021
• Est. primary completion date: October 31, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 99 Years

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

-

Related Conditions & MeSH terms

• Epilepsy

Intervention

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.

Locations

Country	Name	City	State
United States	Thomas Jefferson University	Philadelphia	Pennsylvania

Sponsors (1)

Lead Sponsor	Collaborator
Neuro Event Labs Inc.

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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