EpilepsyNCT06978842
Seizure Prediction Using Wearable EEG
An observational study of Epilepsy, run by Dux Healthcare Inc. at 2 sites in Israel.
Trial summary
This study is a non-interventional clinical trial analyzing EEG recordings from people with epilepsy. Participants wear a comfortable EEG headband at home for several weeks. The goal is to study changes in brain activity that occur before seizures (called "pre-ictal patterns") and to test whether a software algorithm… Read more about this study
- Who is it for?
- Ages 12 years and older
- Conditions the study is for
- Epilepsy
- What is being tested?
- Wearable EEG headband for passive brain signal acquisition
- Type of treatment
- Medical device
- How many people
- 150 participants
- Where
- 2 sitesIsrael
- Study dates
- 19 Jun 2025to Mar 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 12 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study will enroll up to 150 individuals aged 12 years and older with a confirmed diagnosis of epilepsy, who experience between one seizure per day and two seizures over the past three months. Participants must be capable of using a wearable EEG device at home and have access to a compatible smartphone. Recruitment will occur at a tertiary hospital and affiliated community clinics in Israel to ensure diversity in epilepsy severity and demographics. The population includes both adults and adolescents, with safeguards for minors and cognitively impaired participants.
Who may be able to join
- Age: 12 years and older.
- Diagnosis of epilepsy confirmed by EEG, with at least one seizure captured during EEG monitoring by a trained expert.
- Seizure frequency ranging from once per day to two over the last three months preceding inclusion.
- Sufficient cognitive and physical ability (of the participant or caregiver) to comply with the protocol, including device management and data reporting.
- Access to and familiarity with a smartphone capable of running the study application as tested during screening.
- Willingness to provide informed consent and adhere to study procedures.
Who cannot join
- Scalp conditions or physical characteristics preventing proper device fit.
- Any technical or logistical challenges that would prevent reliable EEG data collection or compliance with the study protocol.
- Pregnant or planning a pregnancy during the study.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Israel2 sites, 2 recruiting
- Recruiting
Rambam Medical Center
Haifa
Study Coordinator, +972552999587, n_benisrael@rambam.health.gov.il
- Not yet recruiting
Sheba Medical Center
Ramat Gan
Study Coordinator, +972557751954, Sharona.Salomon@sheba.health.gov.il
About this study
Official title: A Multicenter Prospective Non-Interventional Pilot Feasibility Trial for Seizure Prediction Using Wearable Electroencephalogram Among Patients With Confirmed Epilepsy
This study is a non-interventional clinical trial analyzing EEG recordings from people with epilepsy. Participants wear a comfortable EEG headband at home for several weeks. The goal is to study changes in brain activity that occur before seizures (called "pre-ictal patterns") and to test whether a software algorithm can predict seizures in real-time based on these patterns. No treatments or medications are being tested. The study will help evaluate whether seizure prediction is possible using wearable EEG devices and can support the development of future tools that give patients early warnings before seizures occur.
Read the detailed description
This observational study aims to evaluate the feasibility of real-time seizure prediction using non-invasive, wearable EEG devices in patients with epilepsy. The study focuses on identifying pre-ictal EEG patterns-subtle changes in brain activity that occur prior to seizure onset-and validating a prediction algorithm based on these patterns.
Epileptic seizures often occur unpredictably, significantly affecting patients' quality of life and safety. Existing seizure detection systems operate only after seizure onset. In contrast, predicting seizures before they occur could enable timely interventions, increase patient autonomy, and reduce the risks associated with uncontrolled seizures.
The study involves home use of consumer-grade wearable EEG devices (e.g., BrainBit and Muse headbands), which transmit EEG data via Bluetooth to a mobile app developed by the sponsor. Participants are instructed to wear the device daily for at least 12 weeks. The mobile app provides feedback on signal quality and securely uploads the data to the cloud for analysis. Participants can record seizures through the app, and researchers will also collect medical records for additional clinical annotations when available.
The prediction algorithm being tested uses personalized calibration and advanced statistical control of false alarm rates to ensure clinical viability. The algorithm was initially developed and tested using retrospective hospital-grade EEG data and publicly available datasets. This trial extends that work into the real world, evaluating the algorithm's performance prospectively on wearable data.
Key aims include:
Evaluating the usability of wearable EEG devices for long-term home use in a diverse patient population.
Identifying consistent pre-ictal EEG features within and across patients.
Validating the performance of the seizure prediction algorithm in terms of sensitivity, specificity, and false alarm rate.
Exploring the consistency of pre-ictal patterns across multiple seizures for the same patient.
This feasibility trial is non-interventional and does not alter participants' treatment plans. All data are collected passively and analyzed after being de-identified. Ethics approvals were obtained. The study is expected to contribute critical evidence toward the development of a clinically useful, AI-powered seizure forecasting system for real-world use.
- Conditions
- Epilepsy
- Being tested
- Wearable EEG headband for passive brain signal acquisition
- Keywords
- Seizue, Prediction, Wearable, EEG
- Medical subject headings
- Epilepsy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Wearable EEG headband for passive brain signal acquisitionDevice
This intervention involves the use of non-invasive, consumer-grade wearable EEG headbands to passively record brain activity from individuals with epilepsy in their natural home environments. The devices include BrainBit Headband, BrainBit Mindo, BrainBit Headphones, Muse 2, and Muse S. These devices transmit EEG signals via Bluetooth to a mobile application developed by the sponsor. The app provides real-time feedback on signal quality and securely uploads data to the cloud for offline analysis. The wearable devices are used solely for passive data acquisition and are not being evaluated for safety or therapeutic effectiveness in this study. No changes are made to clinical care or treatment.
What is being measured
The primary outcome is the main question the study is designed to answer.
Seizure prediction sensitivity
Proportion of EEG-labeled seizures correctly predicted by the algorithm within the predefined pre-ictal window.
Measured: At the end of the 12-week monitoring period
Seizure prediction specificity
Proportion of time without seizures correctly classified as non-seizure periods by the algorithm.
Measured: At the end of the 12-week monitoring period
Seizure prediction false alarm rate
Number of false alarms issued by the algorithm 48 hour of EEG monitoring
Measured: At the end of the 12-week monitoring period
Secondary outcomes (8)
System uptime for real-time seizure prediction
Fraction of monitoring time during which the system successfully issues predictions, reflecting adequate EEG signal quality and stable data flow.
Measured: Throughout the 12-week study period
Time Between Algorithm-Predicted Warning and Seizure Onset
The latency between the algorithm's seizure prediction alarm and the actual clinical seizure onset.
Measured: Throughout the 12-week study period
Variability in Prediction Latency Across Events
The standard deviation of the latency between prediction alarm and seizure onset across all predicted seizures
Measured: Throughout the 12-week study period
Wearable EEG device battery and data usage
Quantitative analysis of daily battery consumption and mobile data usage during operation of the wearable EEG and Laura app.
Measured: Throughout the 12-week study period
Participant Adherence to Device Usage, Measured by Daily Wear Time
Mean number of hours per day the wearable EEG device is actively worn and recording, based on device logs. Adherence will be calculated as the percentage of study days in which participants wore the device for at least 8 hours.
Measured: Throughout the 12-week study period
Usability of the wearable EEG system
Participant-reported feedback on device comfort, ease of use, and satisfaction, measured through structured usability questionnaires at baseline, mid-study, and end of study.
Measured: Week 0, Week 6, and Week 12
Frequency of Device-Related Skin Reactions
Number of device-related skin irritation events (e.g., redness, rash, pressure marks) reported by participants or observed by study staff, as recorded in a standardized adverse event log.
Measured: Throughout the 12-week study period
Severity of Device-Related Skin Reactions (Graded by CTCAE v5.0)
Maximum severity grade of each reported skin reaction during the study, based on the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Grades range from 1 (mild) to 5 (death); only Grades 1-3 are expected.
Measured: Throughout the 12-week study period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2025
- Study start19 Jun 2025
- Primary completionMar 2026 (estimated)
- Study completionMar 2026 (estimated)
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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Dux Healthcare Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- rs-ness
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06978842
- Sponsor's protocol ID
- RMB 049-25
- Status last verified
- Aug 2025
- First submitted
- 30 Apr 2025
- Last updated
- 26 Aug 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Prospective Non-Interventional Pilot Feasibility Trial for Seizure Prediction Using Wearable Electroencephalogram Among Patients With Confirmed Epilepsy
- ClinicalTrials.gov ID
- NCT06978842
- Acronym / sponsor ID
- RMB 049-25
- Conditions
- Epilepsy
- Keywords
- Seizue, Prediction, Wearable, EEG
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Dux Healthcare Inc.Industry (pharma, biotech, devices)
- Collaborators
- rs-ness
- Study start
- 19 Jun 2025 (actual)
- Primary completion
- Mar 2026 (estimated)
- Study completion
- Mar 2026 (estimated)
- First posted
- 18 May 2025 (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.
This study is a non-interventional clinical trial analyzing EEG recordings from people with epilepsy. Participants wear a comfortable EEG headband at home for several weeks. The goal is to study changes in brain activity that occur before seizures (called "pre-ictal patterns") and to test whether a software algorithm can predict seizures in real-time based on these patterns. No treatments or medications are being tested. The study will help evaluate whether seizure prediction is possible using wearable EEG devices and can support the development of future tools that give patients early warnings before seizures occur.
Detailed description
This observational study aims to evaluate the feasibility of real-time seizure prediction using non-invasive, wearable EEG devices in patients with epilepsy. The study focuses on identifying pre-ictal EEG patterns-subtle changes in brain activity that occur prior to seizure onset-and validating a prediction algorithm based on these patterns.
Epileptic seizures often occur unpredictably, significantly affecting patients' quality of life and safety. Existing seizure detection systems operate only after seizure onset. In contrast, predicting seizures before they occur could enable timely interventions, increase patient autonomy, and reduce the risks associated with uncontrolled seizures.
The study involves home use of consumer-grade wearable EEG devices (e.g., BrainBit and Muse headbands), which transmit EEG data via Bluetooth to a mobile app developed by the sponsor. Participants are instructed to wear the device daily for at least 12 weeks. The mobile app provides feedback on signal quality and securely uploads the data to the cloud for analysis. Participants can record seizures through the app, and researchers will also collect medical records for additional clinical annotations when available.
The prediction algorithm being tested uses personalized calibration and advanced statistical control of false alarm rates to ensure clinical viability. The algorithm was initially developed and tested using retrospective hospital-grade EEG data and publicly available datasets. This trial extends that work into the real world, evaluating the algorithm's performance prospectively on wearable data.
Key aims include:
Evaluating the usability of wearable EEG devices for long-term home use in a diverse patient population.
Identifying consistent pre-ictal EEG features within and across patients.
Validating the performance of the seizure prediction algorithm in terms of sensitivity, specificity, and false alarm rate.
Exploring the consistency of pre-ictal patterns across multiple seizures for the same patient.
This feasibility trial is non-interventional and does not alter participants' treatment plans. All data are collected passively and analyzed after being de-identified. Ethics approvals were obtained. The study is expected to contribute critical evidence toward the development of a clinically useful, AI-powered seizure forecasting system for real-world use.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 12 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study will enroll up to 150 individuals aged 12 years and older with a confirmed diagnosis of epilepsy, who experience between one seizure per day and two seizures over the past three months. Participants must be capable of using a wearable EEG device at home and have access to a compatible smartphone. Recruitment will occur at a tertiary hospital and affiliated community clinics in Israel to ensure diversity in epilepsy severity and demographics. The population includes both adults and adolescents, with safeguards for minors and cognitively impaired participants.
Key inclusion criteria (6 listed)
- Age: 12 years and older.
- Diagnosis of epilepsy confirmed by EEG, with at least one seizure captured during EEG monitoring by a trained expert.
- Seizure frequency ranging from once per day to two over the last three months preceding inclusion.
- Sufficient cognitive and physical ability (of the participant or caregiver) to comply with the protocol, including device management and data reporting.
- Access to and familiarity with a smartphone capable of running the study application as tested during screening.
Key exclusion criteria (3 listed)
- Scalp conditions or physical characteristics preventing proper device fit.
- Any technical or logistical challenges that would prevent reliable EEG data collection or compliance with the study protocol.
- Pregnant or planning a pregnancy during the study.
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 |
|---|---|---|
| Wearable EEG headband for passive brain signal acquisitionThis intervention involves the use of non-invasive, consumer-grade wearable EEG headbands to passively record brain activity from individuals with epilepsy in their natural home environments. The devices include BrainBit Headband, BrainBit Mindo, BrainBit Headphones, Muse 2, and Muse S. These devices transmit EEG signals via Bluetooth to a mobile application developed by the sponsor. The app provides real-time feedback on signal quality and securely uploads data to the cloud for offline analysis. The wearable devices are used solely for passive data acquisition and are not being evaluated for safety or therapeutic effectiveness in this study. No changes are made to clinical care or treatment. | Device | – |
Primary outcome
Seizure prediction sensitivity
Time frame: At the end of the 12-week monitoring period
Proportion of EEG-labeled seizures correctly predicted by the algorithm within the predefined pre-ictal window.
Seizure prediction specificity
Time frame: At the end of the 12-week monitoring period
Proportion of time without seizures correctly classified as non-seizure periods by the algorithm.
Seizure prediction false alarm rate
Time frame: At the end of the 12-week monitoring period
Number of false alarms issued by the algorithm 48 hour of EEG monitoring
8 secondary outcomes
System uptime for real-time seizure prediction
Time frame: Throughout the 12-week study period
Time Between Algorithm-Predicted Warning and Seizure Onset
Time frame: Throughout the 12-week study period
Variability in Prediction Latency Across Events
Time frame: Throughout the 12-week study period
Wearable EEG device battery and data usage
Time frame: Throughout the 12-week study period
Participant Adherence to Device Usage, Measured by Daily Wear Time
Time frame: Throughout the 12-week study period
Usability of the wearable EEG system
Time frame: Week 0, Week 6, and Week 12
Frequency of Device-Related Skin Reactions
Time frame: Throughout the 12-week study period
Severity of Device-Related Skin Reactions (Graded by CTCAE v5.0)
Time frame: Throughout the 12-week study period
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Aug 2025.
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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.
2 sites 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 Dux Healthcare Inc. at 2 sites in Israel.
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 6 inclusion and 3 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: looking for participants now, as of the sponsor's last update on 26 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Israel. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Seizure prediction sensitivity (time frame: At the end of the 12-week monitoring period). The study lists 3 primary outcomes in all. 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 26 Aug 2025; AzTrials last refreshed its copy on 28 Oct 2026.