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| Name | Class |
|---|---|
| University of Massachusetts, Amherst | OTHER |
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The proposed study is an investigator-initiated study that aims to measure the accuracy of a wearable seizure detection and prediction device (Ear-Seizure Detection Device (EarSD)) by simultaneous recording with conventional video-EEG (Electroencephalogram) on patients with epileptic seizures in the Epilepsy Monitoring Unit of the hospital.
A wearable seizure detection and prediction device (EarSD) is worn by patients with epileptic seizures. In this study, the goal is to validate the accuracy of a newly developed portable seizure detection device by examining if the Ear-SD device can (1) provide more comfort, (2) be unobtrusive to the subject during daily activities, and (3) be able to provide additional insight on a patients' seizure control.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Ear-Worn Group | Experimental | All consented patients admitted to the Epilepsy Monitoring Unit (EMU) who are on continuous EEG (cEEG) will wear the ear-worn seizure detection device (EarSD) and there will be no randomization. The Ear-SD Device will be simultaneously worn by EMU patients on continuous video 21 electrode EEG (International 10-20 system) and single channel electrocardiogram (ECG). Daily skin assessment will be conducted and electrodes will be replaced as needed. At the end of the study, a self-reported short qualitative survey will be conducted to assess the overall experience of the enrolled subjects. The EarSD device and electrodes will be removed at the end of the study with the last skin examination. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ear-SD | Device | The Ear-SD is a purely EEG recording device Continuous Electroencephalogram (cEEG), Electromyogram (EMG), Electrooculogram (EOG), Photoplethysmogram (PPG), Electrodermoactivity (EDA), and Inertial Measurement Unit (IMU). The Ear-SD device rests on the ears and connects to the scalp by two sticker electrodes. |
| Measure | Description | Time Frame |
|---|---|---|
| Seizure Recording Criteria 1 | 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. | Through study completion, an average of 7 Days |
| Seizure Recording Criteria 2 | 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. | Through study completion, an average of 7 Days |
| Seizure Recording Criteria 3 | 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). | Through study completion, an average of 7 Days |
| Data Interpretation | 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)). |
| Measure | Description | Time Frame |
|---|---|---|
| Qualitative Satisfaction Survey | At the end of the study, patients' experience and perception of the EarSD device are collected using a paper-based 7-question survey measured on a 5-point Likert scale ranging from Strongly Disagree to Strongly Agree. A maximum total point score of 35 represents a better reported satisfactory score from participants and having a good experience with the device and its comfortability for daily activities. The survey is a self-administered report, and participants will be asked about the comfortability and perceived utility of the device. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Stephanie Stephens | Contact | 508-856-3939 | Stephanie.Stephens1@umassmed.edu | |
| Charles Hill | Contact | Charles.hill6@umassmed.edu |
| Name | Affiliation | Role |
|---|---|---|
| Felicia Chu, MD | UMass Neurology Department | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Ummmc-Memorial Campus | Recruiting | Worcester | Massachusetts | 01655 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32090969 | Background | Barranco R, Caputo F, Molinelli A, Ventura F. Review on post-mortem diagnosis in suspected SUDEP: Currently still a difficult task for Forensic Pathologists. J Forensic Leg Med. 2020 Feb;70:101920. doi: 10.1016/j.jflm.2020.101920. Epub 2020 Feb 5. | |
| 28139449 | Background | Blachut B, Hoppe C, Surges R, Elger C, Helmstaedter C. Subjective seizure counts by epilepsy clinical drug trial participants are not reliable. Epilepsy Behav. 2017 Feb;67:122-127. doi: 10.1016/j.yebeh.2016.10.036. Epub 2017 Jan 28. |
| Label | URL |
|---|---|
| Epilepsy, accessed: 2023-11-02 | View source |
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The collected de-identified Individual Participant Data (IPD) will be shared with our UMass Amherst collaborators which will include EEG monitoring data, EarSD Device monitoring data, and de-identified collected RedCap Database (start and end of monitoring, replacement of electrodes times, and short qualitative survey). Collected de-identified data of EarSD and cEEG monitoring will go feature extraction and subsequent statistical analysis will be performed by UMass Amherst. The dataset will not be published online or shared with other researchers or presented in a conference or in manuscripts publication. Only a demographic overview of the sample population and results of machine learning algorithms will be submitted for publication. IPD will not be shared or published in any of the articles or papers.
Data will become available after participants have completed the study. And data have been analyzed through the seizure detection Algorithm. Duration 3-5 years; relative to the time when summary data are published or otherwise made available (starting 4-6 months after publication).
Access criteria will include organizations that will review the algorithm-building efficacy: UMass Amherst collaborators/listed staff and UMass Chan research staff. The Institutional Review Board (IRB) will review the requests and approve review according to their policies.
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| ID | Term |
|---|---|
| D012640 | Seizures |
| D004827 | Epilepsy |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| ID | Term |
|---|---|
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D001927 | Brain Diseases |
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| ID | Term |
|---|---|
| D004569 | Electroencephalography |
| ID | Term |
|---|---|
| D003943 | Diagnostic Techniques, Neurological |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D004568 | Electrodiagnosis |
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|
| Electroencephalogram | Diagnostic Test | Standard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG) |
|
| up to 2 years |
| Seizure Accuracy/Prediction | 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. | up to 5 years |
| Through study completion, an average of 7 Days |
| Ummmc-University Campus | Recruiting | Worcester | Massachusetts | 01655 | United States |
|
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