Clinical trial NCT06978842: Seizure Prediction Using Wearable EEG
Official title: A Multicenter Prospective Non-Interventional Pilot Feasibility Trial for Seizure Prediction Using Wearable Electroencephalogram Among Patients With Confirmed Epilepsy
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Epilepsy
Keywords: Seizue
Prediction
Wearable
EEG
Lead sponsor: Dux Healthcare Inc.
Sponsor class: INDUSTRY
Collaborators: name: rs-ness
First posted: 2025-05-18
Enrollment: 150
Sex: ALL
Minimum age (years): 12
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.

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.

Primary outcomes:
measure: Seizure prediction sensitivity
timeFrame: At the end of the 12-week monitoring period
measure: Seizure prediction specificity
timeFrame: At the end of the 12-week monitoring period
measure: Seizure prediction false alarm rate
timeFrame: At the end of the 12-week monitoring period
Secondary outcomes:
measure: System uptime for real-time seizure prediction
timeFrame: Throughout the 12-week study period
measure: Time Between Algorithm-Predicted Warning and Seizure Onset
timeFrame: Throughout the 12-week study period
measure: Variability in Prediction Latency Across Events
timeFrame: Throughout the 12-week study period
measure: Wearable EEG device battery and data usage
timeFrame: Throughout the 12-week study period
measure: Participant Adherence to Device Usage, Measured by Daily Wear Time
timeFrame: Throughout the 12-week study period
measure: Usability of the wearable EEG system
timeFrame: Week 0, Week 6, and Week 12
measure: Frequency of Device-Related Skin Reactions
timeFrame: Throughout the 12-week study period
measure: Severity of Device-Related Skin Reactions (Graded by CTCAE v5.0)
timeFrame: Throughout the 12-week study period
Other outcomes:
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