Wearable Technology for Epilepsy

(Foresight Trial)

Overseen By Empatica Support

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Empatica, Inc.

Disqualifiers: Cognitive impairment, Empatica employees, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

3000 Participants Needed

Trial Summary

What is the purpose of this trial?

The goal of this interventional study is to develop a personalized seizure risk forecast tool in people with epilepsy. The main questions it aims to answer are: * can we develop a future seizure probabilities tool that is more accurate than chance based on the pattern and frequency of previous generalized tonic-clonic seizure (GTCS) events, as well as changes in physiological and behavioral variables. * does this tool improve the lives of people with epilepsy? Researchers will compare a group that does not have access to the forecast tool to a group that does and see if it is accurate and if people with it report that it improved their quality of life.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the SeizureWise algorithm and EmbracePlus-based seizure forecasting tool for epilepsy treatment?

Research shows that wearable devices using algorithms similar to SeizureWise can effectively detect and forecast seizures, improving patient care and quality of life. These devices use sensors to monitor heart rate and other signals, and have been shown to accurately detect seizures and reduce false alarms, which can help prevent unexpected complications. ^{1 2 3 4 5}

Is the wearable technology for epilepsy safe for humans?

The wearable devices for epilepsy monitoring, like the EmbracePlus-based tools, have been used in studies and are generally considered safe for humans. They are non-invasive, meaning they don't require surgery or entering the body, and focus on detecting seizures through external sensors, which are typically safe for long-term use. ^{2 6 7 8 9}

How does the SeizureWise treatment differ from other epilepsy treatments?

The SeizureWise treatment is unique because it uses wearable technology to forecast seizures by monitoring physiological signals like heart rate and electrodermal activity, providing real-time alerts and potentially improving quality of life by reducing the unpredictability of seizures. ^{1 2 4 5 10}

Eligibility Criteria

This trial is for individuals with epilepsy or at risk of it, who can wear the EmbracePlus device on their wrist and follow study procedures. They must understand English and consent to participate. Parents or guardians can consent for them if necessary.

Inclusion Criteria

Subjects or their parents or guardians must understand and consent to be in the study

Subjects or their parents or guardians must be willing and able to comply with study procedures and duration

I have epilepsy or am at risk of developing it.

Exclusion Criteria

Subjects, or their parents or guardians for minor study participants, who do not understand the study and the risks

Subjects who would be placed at undue medical risk associated with any procedure called for in the protocol

Subjects that are Empatica employees and actively participated in the development of this protocol or development of the Seizure Wise algorithms

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Algorithm Development and Testing

An algorithm based on sensor-derived physiological measures is developed and tested with output blinded to the participant.

Approximately 1 year

Randomized Phase

Participants are randomized to two arms, one with access to a seizure forecasting risk score and the other without.

1 year

Multi-day visualizations provided

Follow-up

Participants are monitored for changes in quality of life, psychological health indicators, and seizure management outcomes.

2 years

Treatment Details

Interventions

• SeizureWise algorithm

Trial Overview Researchers are testing a new tool called SeizureWise algorithm that predicts seizure risks by analyzing past seizure patterns and changes in behavior/physiology. Participants will be split into two groups to see if this tool improves accuracy and quality of life.

Participant Groups

Group I: Personalized seizure risk score Experimental Treatment 2 Interventions

The personalized seizure risk score arm will receive multi-day visualization of their sensor derived data and also SeizureWise, an investigational algorithm which enables forecasting of future seizure probabilities based on the pattern and frequency of previous generalized tonic-clonic seizure (GTCS) events, as well as changes in physiological and behavioral variables

Group II: Control Active Control 1 Intervention

The control arm will receive multi-day visualizations of their sensor derived data but not receive SeizureWise

Who Is Running the Clinical Trial?

Empatica, Inc.

Lead Sponsor

Trials

7

Recruited

104,000+

Findings from Research

Wearable devices are becoming increasingly important for epilepsy management, as they can effectively monitor seizure frequency and provide warnings for impending seizures, enhancing patient safety.

Current technology allows for the documentation of nocturnal motor seizures, and various parameters like accelerometry and heart rate are used to capture seizure manifestations, indicating a promising direction for clinical applications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Mobile seizure monitoring in epilepsy patients]. Schulze-Bonhage, A., Böttcher, S., Glasstetter, M., et al.[2020]

Wearable seizure detection devices (WSDDs) can significantly enhance the quality of life for epilepsy patients by improving seizure detection, which may lead to better care and potentially prevent sudden unexpected death in epilepsy.

Among the 16 WSDDs reviewed, those using accelerometry and surface electromyography showed the highest performance in detecting tonic-clonic seizures, emphasizing the need for standardized testing to improve device development and comparison.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Wearable seizure detection devices in refractory epilepsy. Verdru, J., Van Paesschen, W.[2023]

The SeizFt framework, which uses machine learning and wearable SensorDot EEG data, has demonstrated superior seizure detection capabilities, winning first place in the 2023 Seizure Detection Grand Challenge by achieving a 30% improvement over the next best algorithm.

SeizFt's interpretability and the use of meaningful features like delta wave and total absolute power enhance trust among healthcare professionals, making it a promising tool for real-time monitoring and personalized treatment of epilepsy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables. Al-Hussaini, I., Mitchell, CS.[2023]

A new wearable seizure detection system was developed using data from 10 patients, successfully identifying all seizures in 7 patients through a combination of heart rate, oxygen levels, and skin activity, resulting in a very low false positive rate.

The system employs a three-stage detection methodology, where Stage I and II effectively detected seizures in most patients, while Stage III utilized EEG monitoring to identify seizures in patients who were not detected by the first two stages.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multi-Biosignal Analysis for Epileptic Seizure Monitoring. Cogan, D., Birjandtalab, J., Nourani, M., et al.[2019]

Seizure forecasting, which estimates the probability of a seizure occurring, may be more practical than traditional seizure prediction methods, and recent advancements in machine learning have improved these forecasting algorithms.

Over 90% of individuals with epilepsy exhibit circadian rhythms in their seizure patterns, suggesting that factors like stress, heart rate, and sleep quality could serve as noninvasive indicators of seizure susceptibility, potentially enhancing the quality of life for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Seizure forecasting and cyclic control of seizures. Stirling, RE., Cook, MJ., Grayden, DB., et al.[2021]

In a study evaluating four non-EEG seizure monitoring systems for a patient with focal epilepsy, the Emfit mat demonstrated the highest sensitivity (78%) and low false detection rate (0.55 per night), making it the most effective choice for home monitoring.

While some devices showed promising results, they often missed specific seizure types, highlighting the need for personalized assessments and a comprehensive database to guide the selection of monitoring systems based on individual patient needs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures. Van de Vel, A., Verhaert, K., Ceulemans, B.[2019]

The study involved 15 patients with drug-resistant focal seizures and assessed the safety and efficacy of a long-term implanted seizure advisory system, which showed that the device could predict seizure likelihood with sensitivities ranging from 65% to 100% in 11 patients.

While the device had some adverse events, including serious ones like device migration and infection, it demonstrated feasibility for monitoring seizures in ambulatory patients, suggesting potential for improved seizure management if further validated in larger studies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Cook, MJ., O'Brien, TJ., Berkovic, SF., et al.[2022]

The wearable device (Sensor Dot) demonstrated a focal seizure detection sensitivity of 52% in hospitalized patients and 23% in outpatients, indicating that while it can detect some seizures, its overall performance is limited by high false alarm rates and low predictive values.

Patient feedback showed that the device was generally well-received, but side effects led to a dropout rate, highlighting the need for improvements in both device performance and user experience for better clinical implementation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

In-hospital and home-based long-term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience. Macea, J., Bhagubai, M., Broux, V., et al.[2023]

Wearable devices that monitor various physiological signals, such as accelerometry and electrodermal activity, show promise in detecting seizures and providing objective data, which could improve the reliability of seizure monitoring compared to traditional self-reported diaries.

While non-invasive and minimally invasive EEG methods have demonstrated the ability to accurately record seizure activity over long periods, challenges remain in user acceptability and the identification of different seizure types, highlighting the need for further research and development in this area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic. Brinkmann, BH., Karoly, PJ., Nurse, ES., et al.[2021]

A wearable sensor system that measures electrodermal activity and motion can effectively detect various types of seizures, achieving an overall sensitivity of 89.1% and specificity of 93.1% in a study involving 8 patients.

For seizures without motor activity, the system demonstrated even higher sensitivity at 97.1%, indicating its potential for reliable long-term monitoring of epilepsy outside of hospital settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy. Heldberg, BE., Kautz, T., Leutheuser, H., et al.[2020]

References

1. Germany pubmed.ncbi.nlm.nih.gov

[Mobile seizure monitoring in epilepsy patients]. [2020]

2. Italy pubmed.ncbi.nlm.nih.gov

Wearable seizure detection devices in refractory epilepsy. [2023]

3. Switzerland pubmed.ncbi.nlm.nih.gov

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables. [2023]

4. Singapore pubmed.ncbi.nlm.nih.gov

Multi-Biosignal Analysis for Epileptic Seizure Monitoring. [2019]

5. United States pubmed.ncbi.nlm.nih.gov

Seizure forecasting and cyclic control of seizures. [2021]

6. United States pubmed.ncbi.nlm.nih.gov

Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures. [2019]

7. England pubmed.ncbi.nlm.nih.gov

Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

In-hospital and home-based long-term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience. [2023]

9. Switzerland pubmed.ncbi.nlm.nih.gov

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy. [2020]