Ear-Seizure Detection (EarSD) Study for Seizure Monitoring
This study is testing a new wearable device called Ear-SD. This device records different body signals like brain waves (EEG), muscle activity (EMG), and eye movements (EOG) to see if it can accurately detect and predict seizures. Researchers want to see if the Ear-SD device is comfortable, easy to use during daily activities, and provides helpful information about seizure control. You might be able to join if you are 18 or older, are admitted to the UMass Memorial Epilepsy Monitoring Unit for long-term video-EEG monitoring, and are willing to wear the device. The study aims to enroll 40 participants, but its current recruitment status is unclear.
- Study design
- This interventional study plans to enroll 40 participants. It is testing the Ear-SD device against standard video-EEG monitoring.
- What's involved
- You would wear the Ear-SD device while undergoing standard video-EEG monitoring in the Epilepsy Monitoring Unit for an average of 7 days.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary endpoints are measured through study completion, an average of 7 days.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Ear-Seizure Detection (EarSD) Study
At a glance
Conditions
Where it's being run
2 sites across 1 statesStudy leadership
- Felicia Chu, MD · PRINCIPAL_INVESTIGATOR · UMass Neurology Department
Who to contact
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What this trial measures
- Seizure Recording Criteria 1Through study completion, an average of 7 Days
Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include number of seizure events per participant.
- Seizure Recording Criteria 2Through study completion, an average of 7 Days
Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include average duration of each seizure in minutes and seconds and total recording time in hours aggregated to arrive at one reported value seizure classification.
- Seizure Recording Criteria 3Through study completion, an average of 7 Days
Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include reported value seizure classification. Seizure classification includes Unclassified (UC), Focal Onset Aware (FOA), Focal Onset Impaired (FOIA), Focal to Bilateral Tonic-Clonic (FBTC).
- Data Interpretationup to 2 years
EarSD extracted EEG signals from the log file plotted alongside EDF files from cEEG are measured and compared to detect seizure onset and offset times for data interpretation. Two-minute segments of cEEG European Data Format (EDF) consisting of non-seizure signals from periods before and after the seizures, and non-seizure signals from periods of daily activities like talking, eating, and walking are involved in the comparison to detect seizure onset and offset times. Prediction measurement of Seizure Sensitivity (SS) and False Positivity Rate per hour (FPR/h) are measured from the recorded data signals. Seizure Sensitivity (SS) is the ratio between the (number of predicted seizures)/(total number of seizures) = (number of true alarms)/(total number of seizures). FPR/h is the number of alarms that do not correspond to seizures raised in one hour. FPR/h = ((Number of false alarms/Interictal Duration) - (Number of False Alarms × Refractory period)).
- Seizure Accuracy/Predictionup to 5 years
EarSD recordings from each electrode are separated and filtered to eliminate noise and artifact and results in 12 output signals (6 signals/ear) for comparison against cEEG EDF files for accuracy and precision. Mean, standard and average deviation, skewness, kurtosis, lowest and highest value, and the root mean square amplitude are measured from the dataset and are normalized between 0 and 1 then passed into the seizure detection and prediction Machine Learning (ML) model. ML model consisting of algorithms using deep neural networks (DNN), recurrent neural networks (RNNs) and Long Short-Term Memory networks (LSTM), classifies whether the signals are a seizure signal vs non-seizure signal, the focal type (left side/right side) and predicts the accuracy of seizures a minute ahead with the goal of achieving 96 percent or better accuracy and reducing the number of false positives.