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

NCT ID: NCT06705439

Last Updated: 2024-12-03

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

50 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-11-15

Study Completion Date

2025-06-30

Brief Summary

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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

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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

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Epilepsy

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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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

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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

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Neuro Event Labs Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Selim Benbadis, MD

Role: PRINCIPAL_INVESTIGATOR

University of South Florida

Locations

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Tampa General Hospital

Tampa, Florida, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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US Agent

Role: CONTACT

[email protected]
+1 (210) 708-0667

Facility Contacts

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Marina Azevedo, Nurse

Role: primary

[email protected]
+18132502323

Selim Benbadis, MD

Role: backup

Other Identifiers

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STUDY005226

Identifier Type: -

Identifier Source: org_study_id