EpilepsyNCT05606575
A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning - Nelli
An observational study of Epilepsy, run by Neuro Event Labs Inc. at 1 site in United States.
Trial summary
Nelli is a video-based non-EEG physiological seizure monitoring system. This study is a blinded comparison of Nelli's identified events to gold-standard video EEG review in at-rest pediatric subjects with suspected motor seizures. Read more about this study
- Who is it for?
- Ages 6 to 21 years
- Conditions the study is for
- Epilepsy
- What is being tested?
- Nelli
- Type of treatment
- Medical device
- How many people
- 150 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Aug 2022to 31 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 6 to 21 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Patients aged 6-21 with history (or suspected history) of motor seizures that are undergoing video-EEG monitoring for routine clinical care.
Who may be able to join
- Subject shall sign informed consent.
- Subject is between 6 and 21 years.
- Subjects shall be undergoing video-EEG monitoring for routine clinical purposes.
- Subjects shall have a suspected history of motor seizures.
- Subject shall be able to understand and sign written informed consent or have a legally authorized representative (LAR) who can do so, prior to the performance of any study assessments.
Who cannot join
- None identified.
Trial locations and contacts
1 site in 1 country.
United States1 site
The University of Tennessee Health Science Center
Memphis, Tennessee, 38163
About this study
Nelli is a video-based non-EEG physiological seizure monitoring system. This study is a blinded comparison of Nelli's identified events to gold-standard video EEG review in at-rest pediatric subjects with suspected motor seizures.
Read the detailed description
Automated analysis of video recordings to detect seizures, assisted by modern methods of machine learning, holds great promise to address this issue. Increased computational power has made it possible to implement complex image recognition tasks and machine learning in everyday use. Nelli® software is designed to use computer vision and machine learning-based algorithms to automatically detect seizure events. This study will provide evidence that Nelli software can identify seizure events and deliver objective data to clinicians for evaluation of seizure management.
This study is being conducted to validate the Nelli Software's ability to identify periods of audio
/video data that contain recordings of patients experiencing seizures (or seizure-like events) during periods of rest. The software's performance will be compared to the gold standard, expert review of video EEG 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% (lower 95% CI) and with a false discovery rate (FDR) comparable to similar devices on the market.
- Conditions
- Epilepsy
- Being tested
- Nelli
- Medical subject headings
- Epilepsy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
NelliDevice
Nelli is a non-EEG physiological signal-based seizure detection and quantification device that is indicated for use as an adjunct to seizure monitoring during periods of rest. The device utilizes automated analysis of audio and video (media) data collected via the personal recording unit (PRU) hardware accessory to identify epileptic and non-epileptic seizure events with a positive motor component.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of a seizure detection system
To show that Nelli is able to correctly identify each category of seizures separately (Category I, II, and III) and all seizures categories combined with a sensitivity of at least 70%. Hypotheses will be tested sequentially (all seizures combined, Category I, then Category II, then Category III), each with a significance level of 2.5%, and will continue until the first hypothesis is not rejected.
For each detected abnormal event, the probability is calculated and concluded as seizure/non- seizure using predefined threshold values, pre-trained seizure detection library, and probability of that event. The time-points are reported automatically into the Dashboard of Nelli. Statistical analyses will be performed to calculate true and false positive and negative detection rates.
Measured: During routine video-EEG monitoring, up to 14 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Nov 2022
- Study start1 Aug 2022
- Primary completion31 Dec 2025
- Study completion31 Jan 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for EpilepsyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 150 participants (actual)
Sponsor and investigators
- Lead sponsor
- Neuro Event Labs Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- James Wheless, MD, Principal investigator, The University of Tennessee Health Science Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05606575
- Sponsor's protocol ID
- 21-08296-XP
- Status last verified
- Feb 2026
- First submitted
- 25 Oct 2022
- Last updated
- 13 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning - Nelli
- ClinicalTrials.gov ID
- NCT05606575
- Acronym / sponsor ID
- 21-08296-XP
- Conditions
- Epilepsy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 150 participants (actual)
- Lead sponsor
- Neuro Event Labs Inc.Industry (pharma, biotech, devices)
- Study start
- 1 Aug 2022 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Jan 2026 (actual)
- First posted
- 4 Nov 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Nelli is a video-based non-EEG physiological seizure monitoring system. This study is a blinded comparison of Nelli's identified events to gold-standard video EEG review in at-rest pediatric subjects with suspected motor seizures.
Detailed description
Automated analysis of video recordings to detect seizures, assisted by modern methods of machine learning, holds great promise to address this issue. Increased computational power has made it possible to implement complex image recognition tasks and machine learning in everyday use. Nelli® software is designed to use computer vision and machine learning-based algorithms to automatically detect seizure events. This study will provide evidence that Nelli software can identify seizure events and deliver objective data to clinicians for evaluation of seizure management.
This study is being conducted to validate the Nelli Software's ability to identify periods of audio
/video data that contain recordings of patients experiencing seizures (or seizure-like events) during periods of rest. The software's performance will be compared to the gold standard, expert review of video EEG 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% (lower 95% CI) and with a false discovery rate (FDR) comparable to similar devices on the market.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 6 to 21 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Patients aged 6-21 with history (or suspected history) of motor seizures that are undergoing video-EEG monitoring for routine clinical care.
Key inclusion criteria (5 listed)
- Subject shall sign informed consent.
- Subject is between 6 and 21 years.
- Subjects shall be undergoing video-EEG monitoring for routine clinical purposes.
- Subjects shall have a suspected history of motor seizures.
- Subject shall be able to understand and sign written informed consent or have a legally authorized representative (LAR) who can do so, prior to the performance of any study assessments.
Key exclusion criteria (1 listed)
- None identified.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| NelliNelli is a non-EEG physiological signal-based seizure detection and quantification device that is indicated for use as an adjunct to seizure monitoring during periods of rest. The device utilizes automated analysis of audio and video (media) data collected via the personal recording unit (PRU) hardware accessory to identify epileptic and non-epileptic seizure events with a positive motor component. | Device | – |
Primary outcome
Sensitivity of a seizure detection system
Time frame: During routine video-EEG monitoring, up to 14 days
To show that Nelli is able to correctly identify each category of seizures separately (Category I, II, and III) and all seizures categories combined with a sensitivity of at least 70%. Hypotheses will be tested sequentially (all seizures combined, Category I, then Category II, then Category III), each with a significance level of 2.5%, and will continue until the first hypothesis is not rejected. For each detected abnormal event, the probability is calculated and concluded as seizure/non- seizure using predefined threshold values, pre-trained seizure detection library, and probability of that event. The time-points are reported automatically into the Dashboard of Nelli. Statistical analyses will be performed to calculate true and false positive and negative detection rates.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2026.
- Individual participant dataSharing planned: No
Related trials for Epilepsy
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
James Wheless, MD principal investigator
The University of Tennessee Health Science Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Epilepsy, run by Neuro Event Labs Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of a seizure detection system (time frame: During routine video-EEG monitoring, up to 14 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2026.