Epilepsy
Conditions
Keywords
Seizue, Prediction, Wearable, EEG
Brief 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 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
DEVICEWearable 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. 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.
Sponsors
rs-ness
Dux Healthcare Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
12 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Seizure prediction sensitivity | 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 | 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 | At the end of the 12-week monitoring period | Number of false alarms issued by the algorithm 48 hour of EEG monitoring |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Wearable EEG device battery and data usage | Throughout the 12-week study period | Quantitative analysis of daily battery consumption and mobile data usage during operation of the wearable EEG and Laura app. |
| Participant Adherence to Device Usage, Measured by Daily Wear Time | Throughout the 12-week study period | 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. |
| System uptime for real-time seizure prediction | Throughout the 12-week study period | Fraction of monitoring time during which the system successfully issues predictions, reflecting adequate EEG signal quality and stable data flow. |
| Frequency of Device-Related Skin Reactions | Throughout the 12-week study period | 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. |
| Severity of Device-Related Skin Reactions (Graded by CTCAE v5.0) | Throughout the 12-week study period | 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. |
| Usability of the wearable EEG system | Week 0, Week 6, and Week 12 | Participant-reported feedback on device comfort, ease of use, and satisfaction, measured through structured usability questionnaires at baseline, mid-study, and end of study. |
| Time Between Algorithm-Predicted Warning and Seizure Onset | Throughout the 12-week study period | The latency between the algorithm's seizure prediction alarm and the actual clinical seizure onset. |
| Variability in Prediction Latency Across Events | Throughout the 12-week study period | The standard deviation of the latency between prediction alarm and seizure onset across all predicted seizures |
Countries
Israel
Outcome results
None listed