Actively Recruiting

Phase Not Applicable
Age: 18Years +
All Genders
Healthy Volunteers
ID06598189

Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System

Led by Felicia Chu · Updated on 2025-10-28

40

Participants Needed

2

Research Sites

261 weeks

Total Duration

On this page

Sponsors

F

Felicia Chu

Lead Sponsor

U

University of Massachusetts, Amherst

Collaborating Sponsor

AI-Summary

What this Trial Is About

Researchers are evaluating a new wearable device called the Ear-Seizure Detection Device (EarSD) designed to detect and predict epileptic seizures. This study aims to validate how accurately the EarSD device records seizures compared to the standard video-electroencephalogram (video-EEG) monitoring used in the Epilepsy Monitoring Unit (EMU). The goal is also to see if the device offers more comfort, is less intrusive during daily activities, and provides additional insights into seizure control for patients with epilepsy. Participants admitted to the EMU who are undergoing continuous video 21-electrode EEG and electrocardiogram (ECG) monitoring will wear the EarSD device simultaneously. The EarSD is an ear-worn device that records multiple signals including EEG, electromyogram, electrooculogram, photoplethysmogram, electrodermoactivity, and inertial motion data. There is no randomization; all enrolled patients will use the device during their EMU stay. Daily skin checks and electrode replacements will be done as needed, and the device will be removed at the end of the study period. During the study, participants will be monitored for an average of 7 days while wearing the EarSD and undergoing standard video-EEG monitoring. Researchers will assess seizure detection accuracy and prediction capabilities over a longer term, up to 5 years, and analyze data interpretation for up to 2 years. Participants will also complete a short survey about their experience wearing the device. Safety monitoring includes daily skin assessments to check for irritation, ensuring participant comfort throughout the study.

CONDITIONS

Brief Title

Ear-Seizure Detection (EarSD) Study

Who Can Participate

Age: 18Years +
All Genders
Healthy Volunteers

Eligibility Criteria

Eligible

You may qualify if you...

  • Age 18 years or older
  • Admitted to the Epilepsy Monitoring Unit for long-term video-EEG monitoring
  • Diagnosed with focal or generalized epilepsy
  • Willing to wear the ear-worn seizure detection device
  • Able to provide informed consent
Not Eligible

You will not qualify if you...

  • Wearing other ear devices such as hearing aids
  • Unable or unwilling to provide informed consent
  • Skin irritation where the device would be placed
  • Patients with intracranial electrode placement
  • Prisoners
  • Cognitive impairment
  • Pregnant women
  • Children under 18 years old

AI-Screening

AI-Powered Screening

Complete this quick 3-step screening to check your eligibility

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Your Study Journey

Screening

Duration - 2 to 4 weeks

Participants are screened for eligibility to participate in the trial.

1 visit (in-person)

Diagnostic Evaluation

Duration - Approximately 7 days

Participants wear the Ear-SD device simultaneously with continuous video EEG and ECG monitoring to assess seizure detection accuracy.

Continuous monitoring during hospital stay with daily skin assessments and electrode replacements as needed

Long-term Monitoring

Duration - Up to 5 years

Data interpretation and seizure prediction analysis are conducted using collected data over an extended period.

Periodic assessments depending on data analysis schedule

Trial Site Locations

Total: 2 locations

1

Ummmc-Memorial Campus

Worcester, Massachusetts, United States, 01655

Actively Recruiting

2

Ummmc-University Campus

Worcester, Massachusetts, United States, 01655

Actively Recruiting

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Research Team

S

Stephanie Stephens

C

Charles Hill

How is the study designed?

Study Type

INTERVENTIONAL

Masking

NONE

Allocation

NA

Model

SINGLE_GROUP

Primary Purpose

DIAGNOSTIC

Number of Arms

1

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Published Research Related To This Trial

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https://pubmed.ncbi.nlm.nih.gov/32090969

Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform.

Ali Shahidi Zandi, Manouchehr Javidan, Guy A Dumont...

https://pubmed.ncbi.nlm.nih.gov/20659825

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.

Kaat Vandecasteele, Thomas De Cooman, Christos Chatzichristos...

https://pubmed.ncbi.nlm.nih.gov/34240748