Seizure Prediction Using Wearable EEG
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Wearable EEG headband for passive brain signal acquisition.
- Who it may be relevant to
- Registry conditions: Epilepsy. Basic parameters: from 12 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Israel
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
A Multicenter Prospective Non-Interventional Pilot Feasibility Trial for Seizure Prediction Using Wearable Electroencephalogram Among Patients With Confirmed Epilepsy
Overview
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.
Interventions
- Device Wearable EEG headband for passive brain signal acquisition
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
Primary outcome measures
- Seizure prediction sensitivity [Time frame: At the end of the 12-week monitoring period]
- Seizure prediction specificity [Time frame: At the end of the 12-week monitoring period]
- Seizure prediction false alarm rate [Time frame: At the end of the 12-week monitoring period]
Secondary outcome measures (8)
- 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]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Israel · 2 centers
- Rambam Medical Center — Haifa
- Sheba Medical Center — Ramat Gan
Identifiers
NCT: NCT06978842 · RMB 049-25